Fast followers?/Speed contagion: assessing the impact of Montreal F1 Grand Prix on high-speed ticketing rates (2000-2022)

Step 1. Weather variables. We defined the dates by year and availability (whether the event took place or not), days of the week, time windows, and pre and post-event spans, which we then linked to nearby meteorological stations. Also we linked with Collisions data.

# remove objects and memory
rm(list=ls());gc()

          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  837600 44.8    1636887 87.5  1167794 62.4
Vcells 1709049 13.1    8388608 64.0  3475595 26.6

#remove images
while(!dev.cur())dev.off()
cat("\014")

Load libraries and data

Particularly, the weathercan library, which contains information on climate change and environmental variables from stations across Canada¹.

#borrar caché
#system("fc-cache -f -v")

#check R version
if(Sys.info()["sysname"]=="Windows"){
if (getRversion() != "4.4.1") { stop("Requiere versión de R 4.4.1. Actual: ", getRversion()) }
}
if(Sys.info()["sysname"]=="Linux"){
if (getRversion() != "4.4.1") { stop("Requiere versión de R 4.4.1. Actual: ", getRversion()) }
}
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# install.packages(c("dplyr", #for data
#                    "tidyr", #for data
#                    "lubridate",  #for dates
#                    "openxlsx", #for excel files
#                    "rio", #for importing and exporting data
#                    "purrr", #for iterating in databases
#                    "devtools", #for external packages
#                    "DiagrammeR", #para visualizar DAG
#                    "dagitty",
#                    "ggdag",
#                    "ggplot2" #for graphics
#                    "kableExtra", #pretty tables
#                    "quarto", #for documents
#                    "geosphere" #for coordinates and classifying
#                    "geepack", #for regression
#                    "glmmTMB", #For GLMMs
#                    "DHARMa", #For residual diagnostics
#                    "car", #For hypothesis testing
#                    "brms", #Bayesian model
#                    "bayesplot",
#                    "loo",
#                    "Synth", #for synthetic control method
#                    "weathercan",#for weather data
#                    "sandwich", #cluster robust intervals
#                    "emmeans", #for predictions
#                    "gnm", #Conditional Poisson models
#                    "splines", #nonlinearity 
#                    "geeM", #negative binomial and more flexible GEE models
#                    "PanelMatch", #Matching technique with panel data
#                    "scpi" #control sintético
#                    "nixtlar", #for time series analysis and prediction
#                    "CausalImpact" #for time series causal impact
#                    "forecast" #for time series analysis prediction and decomposition
#                    ))
library(dplyr); library(lubridate); library(tidyverse); library(openxlsx); library(rio); library(purrr); library(dagitty); library(ggdag); library(kableExtra); library(geosphere); library(geepack); library(lme4); library(glmmTMB); library(DHARMa); library(car); library(brms); library(bayesplot); library(loo); library(Synth);  library(weathercan); library(sandwich); library(emmeans); library(gnm); library(splines); library(geeM); library(plm); library(PanelMatch); library(scpi); library(fect); library(nixtlar); library(CausalImpact); library(forecast); library(webshot)

Adjuntando el paquete: 'dplyr'

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Warning: package 'readr' was built under R version 4.4.3

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ forcats 1.0.0     ✔ stringr 1.5.1
✔ ggplot2 3.5.2     ✔ tibble  3.2.1
✔ purrr   1.0.4     ✔ tidyr   1.3.1
✔ readr   2.1.5     

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

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This is DHARMa 0.4.7. For overview type '?DHARMa'. For recent changes, type news(package = 'DHARMa')

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Cargando paquete requerido: Rcpp

Loading 'brms' package (version 2.22.0). Useful instructions
can be found by typing help('brms'). A more detailed introduction
to the package is available through vignette('brms_overview').


Adjuntando el paquete: 'brms'


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This is bayesplot version 1.12.0

- Online documentation and vignettes at mc-stan.org/bayesplot

- bayesplot theme set to bayesplot::theme_default()

   * Does _not_ affect other ggplot2 plots

   * See ?bayesplot_theme_set for details on theme setting


Adjuntando el paquete: 'bayesplot'


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This is loo version 2.8.0

- Online documentation and vignettes at mc-stan.org/loo

- As of v2.0.0 loo defaults to 1 core but we recommend using as many as possible. Use the 'cores' argument or set options(mc.cores = NUM_CORES) for an entire session. 

- Windows 10 users: loo may be very slow if 'mc.cores' is set in your .Rprofile file (see https://github.com/stan-dev/loo/issues/94).

##
## Synth Package: Implements Synthetic Control Methods.


## See https://web.stanford.edu/~jhain/synthpage.html for additional information.

Warning: package 'weathercan' was built under R version 4.4.3

As of v0.7.2, the `normals` column in `stations()` reflects whether or not there
are *any* normals available (not just the most recent).
Welcome to emmeans.
Caution: You lose important information if you filter this package's results.
See '? untidy'
Registered S3 method overwritten by 'lfe':
  method    from 
  nobs.felm broom

Adjuntando el paquete: 'plm'

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Registered S3 method overwritten by 'GGally':
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  +.gg   ggplot2
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######################### Warning from 'xts' package ##########################
#                                                                             #
# The dplyr lag() function breaks how base R's lag() function is supposed to  #
# work, which breaks lag(my_xts). Calls to lag(my_xts) that you type or       #
# source() into this session won't work correctly.                            #
#                                                                             #
# Use stats::lag() to make sure you're not using dplyr::lag(), or you can add #
# conflictRules('dplyr', exclude = 'lag') to your .Rprofile to stop           #
# dplyr from breaking base R's lag() function.                                #
#                                                                             #
# Code in packages is not affected. It's protected by R's namespace mechanism #
# Set `options(xts.warn_dplyr_breaks_lag = FALSE)` to suppress this warning.  #
#                                                                             #
###############################################################################

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#special repository indicated or the package
if(!require(weathercan)){
   install.packages("weathercan", 
                  repos = c("https://ropensci.r-universe.dev", "https://cloud.r-project.org")); library(weathercan)
  }

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if(!require(bpmn)){devtools::install_github("bergant/bpmn")}

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#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
sum_dates <- function(x){
 
  cbind.data.frame(
    min= as.Date(min(unclass(as.Date(x)), na.rm=T), origin = "1970-01-01"),
    p001= as.Date(quantile(unclass(as.Date(x)), .001, na.rm=T), origin = "1970-01-01"),
    p005= as.Date(quantile(unclass(as.Date(x)), .005, na.rm=T), origin = "1970-01-01"),
    p025= as.Date(quantile(unclass(as.Date(x)), .025, na.rm=T), origin = "1970-01-01"),
    p25= as.Date(quantile(unclass(as.Date(x)), .25, na.rm=T), origin = "1970-01-01"),
    p50= as.Date(quantile(unclass(as.Date(x)), .5, na.rm=T), origin = "1970-01-01"),
    p75= as.Date(quantile(unclass(as.Date(x)), .75, na.rm=T), origin = "1970-01-01"),
    p975= as.Date(quantile(unclass(as.Date(x)), .975, na.rm=T), origin = "1970-01-01"),
    p995= as.Date(quantile(unclass(as.Date(x)), .995, na.rm=T), origin = "1970-01-01"),
    p999= as.Date(quantile(unclass(as.Date(x)), .999, na.rm=T), origin = "1970-01-01"),
    max= as.Date(max(unclass(as.Date(x)), na.rm=T), origin = "1970-01-01")
  )
}
smd_bin <- function(x,y){
  z <- x*(1-x)
  t <- y*(1-y)
  k <- sum(z,t)
  l <- k/2
  
  return((x-y)/sqrt(l))
  
}

theme_custom_sjplot2 <- function(base_size = 12, base_family = "") {
  theme_minimal(base_size = base_size, base_family = base_family) +
    theme(
      # Text elements
      text = element_text(size = base_size, family = base_family),
      plot.title = element_text(face = "bold", hjust = 0.5, size = base_size * 1.2),
      plot.subtitle = element_text(hjust = 0.5, margin = margin(b = 10)),
      axis.title = element_text(size = base_size, face = "bold"),
      axis.text = element_text(size = base_size * 0.8),
      axis.text.x = element_text(angle = 0, hjust = 0.5, vjust = 0.5),
      axis.text.y = element_text(angle = 0, hjust = 1, vjust = 0.5),
      axis.title.x = element_text(margin = margin(t = 10)),
      axis.title.y = element_text(margin = margin(r = 10)),
      
      # Plot layout
      plot.margin = margin(t = 20, r = 20, b = 20, l = 20),
      panel.grid.major = element_line(color = "grey80"),
      panel.grid.minor = element_blank(),
      legend.position = "right",
      legend.text = element_text(size = base_size * 0.8),
      legend.title = element_text(size = base_size, face = "bold"),
      legend.background = element_rect(fill = "white", colour = NA),
      legend.box.background = element_rect(colour = "grey80", linetype = "solid"),
      legend.key = element_rect(fill = "white", colour = "white")
    )
}

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num_cores <- parallel::detectCores() -1
data.table::setDTthreads(threads = num_cores)#restore_after_fork = NULL, throttle = NULL)

#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
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options(scipen=2) #display numbers rather scientific number


nixtlar::nixtla_client_setup(api_key = readLines(paste0(gsub("f1/","", getwd() ),"/key.txt"))[[2]])

Warning in readLines(paste0(gsub("f1/", "", getwd()), "/key.txt")): incomplete
final line found on 'H:/Mi unidad/PERSONAL
ANDRES/UCH_salud_publica/pasantia/f1/key.txt'

API key has been set for the current session.

Get the F1 race calendar 1978-2024. Since 2000 the race almost always falls on the second Sunday of June (seven times) or the first Sunday (five times). Some outliers occurred in 2006 (25 Jun), when the event was pushed two weeks later to balance the calendar; in 2022 (19 Jun) the event slipped a week because of the re-shuffled post-COVID schedule. In 2009 it was cancelled due to a commercial dispute, and in 2020-21, pandemic travel restrictions imposed the non-realization of the event².

races <- tibble::tribble(
  ~year, ~race_date,
  1978, "1978-10-08",
  1979, "1979-09-30",
  1980, "1980-09-28",
  1981, "1981-09-27",
  1982, "1982-06-13",
  1983, "1983-06-12",
  1984, "1984-06-17",
  1985, "1985-06-16",
  1986, "1986-06-15",
  1987, NA_character_,   # cancelada
  1988, "1988-06-12",
  1989, "1989-06-18",
  1990, "1990-06-10",
  1991, "1991-06-02",
  1992, "1992-06-14",
  1993, "1993-06-13",
  1994, "1994-06-12",
  1995, "1995-06-11",
  1996, "1996-06-16",
  1997, "1997-06-15",
  1998, "1998-06-07",
  1999, "1999-06-13",
  2000, "2000-06-18",
  2001, "2001-06-10",
  2002, "2002-06-09",
  2003, "2003-06-15",
  2004, "2004-06-13",
  2005, "2005-06-12",
  2006, "2006-06-25",
  2007, "2007-06-10",
  2008, "2008-06-08",
  2009, NA_character_,   # cancelada
  2010, "2010-06-13",
  2011, "2011-06-12",
  2012, "2012-06-10",
  2013, "2013-06-09",
  2014, "2014-06-08",
  2015, "2015-06-07",
  2016, "2016-06-12",
  2017, "2017-06-11",
  2018, "2018-06-10",
  2019, "2019-06-09",
  2020, NA_character_,   # cancelada
  2021, NA_character_,   # cancelada
  2022, "2022-06-19",
  2023, "2023-06-18",
  2024, "2024-06-09"
) %>% 
  mutate(race_date = as_date(race_date))

Metheorological variables

We created a named character vector called station_patterns as a lookup table where each short, convenient name (like trudeau or mctavish) is directly linked to the full, official name of a specific Canadian weather station from Environment and Climate Change Canada. 10 different weather stations are being identified. These stations represent a variety of locations, including a major international airport (trudeau), an urban center (mctavish), and various points across a broader geographic area (e.g., west, northwest, northeast of a specific island, and surrounding regions like Laval and the South Shore). This approach makes your code more readable and easier to maintain, as you can refer to complex station names using simple aliases, and any future changes to official names only require an update in this single, centralized location.

After 2025-06-12, we added 8 stations explicitly located in Longueuil (2), Trois-Rivières (1), Québec (1) and Sherbrooke (4).

station_patterns <- c(
  trudeau          = "MONTREAL/PIERRE ELLIOTT TRUDEAU INTL",   # Dorval :contentReference[oaicite:0]{index=0}
  mctavish         = "MCTAVISH",                               # centro urbano :contentReference[oaicite:1]{index=1}
  ste_anne         = "STE-ANNE-DE-BELLEVUE 1",                 # extremo-oeste :contentReference[oaicite:2]{index=2}
  ste_genevieve    = "STE GENEVIEVE",                          # noroeste isla :contentReference[oaicite:3]{index=3}
  rdp              = "RIVIERE DES PRAIRIES",                   # noreste isla :contentReference[oaicite:4]{index=4}
  lave_auteuil     = "AUTEUIL",                                # Laval norte :contentReference[oaicite:5]{index=5}
  ste_therese      = "STE THERESE OUEST",                      # al N de Laval :contentReference[oaicite:6]{index=6}
  lave_dorothee    = "STE DOROTHEE",                           # Laval oeste (serie vigente)
  st_hubert        = "MONTREAL/ST-HUBERT A",                   # aeropuerto Longueuil :contentReference[oaicite:7]{index=7}
  la_prairie       = "LAPRAIRIE",                               # Rive-Sud suroeste :contentReference[oaicite:8]{index=8}
  vercheres        = "VERCHERES",
  st_amable        = "ST AMABLE"
)

#Before 2025-06-12, stations covers the urban areas of Laval, Montreal, and Longueuil 
#added missing stations explicitly located in:
#Trois-Rivières
#Québec
#Sherbrooke
#
station_patterns_rec <- c(
  station_patterns,
  trois_rivieres    = "TROIS RIVIERES AQUEDUC",    # Aqueduc Trois-Rivières (≈10 km from YRQ)
  quebec_airport    = "QUEBEC/JEAN LESAGE INTL",  # Québec City Airport
  sherbrooke_air    = "SHERBROOKE A",             # Sherbrooke Airport
  # sherbrooke_city = "SHERBROOKE",               # Sherbrooke urban center (if needed)
  lennoxville       = "LENNOXVILLE",              # Lennoxville (near Sherbrooke)
  vercheres         = "VERCHERES",                # Verchères (south shore, covers May 2019)
  st_amable         = "ST AMABLE",                # Saint-Amable (south shore)
  sawyerville_nord  = "SAWYERVILLE NORD",         # Sawyerville Nord (≤25 km from Sherbrooke A)
  bromptonville     = "BROMPTONVILLE",            # Bromptonville (≤25 km from Sherbrooke A)
  st_hubert2        = "MONTREAL/ST-HUBERT"        # New St-Hubert hourly automated station
)

We get a glimpse of stations, we got the daily measurements for each station.

stations_df <- purrr::imap_dfr(
  station_patterns_rec,
  function(pattern, alias) {
    out <- stations_search(pattern) %>%           # busca por cadena
      filter(interval %in% c("day")) %>%  # prioriza estaciones operativas
      slice(1)                                    # toma la coincidencia más cercana
    if (nrow(out) == 0)
      stop(sprintf("No se encontró ninguna estación para «%s»", pattern), call. = FALSE)
    out %>% mutate(alias = alias) %>%             # añade el nombre “amistoso”
      select(alias, station_id, start, end, station_name)
  }
)

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

We set up geographical information for 10 weather stations and 3 major urban centers, then calculates which of those 3 cities is geographically closest to each weather station (using Haversine distance). After this, we categorize stations based on proximity.

# 1. Define your stations
stations <- data.frame(
  station = c(
    names(station_patterns)[1:10],
    "trois_rivieres", "quebec_airport", "sherbrooke_air", "lennoxville", "vercheres", "st_amable", "sawyerville_nord", 
"bromptonville", "st_hubert2"
  ),
  lat = c(
    45.470556, 45.500000, 45.430000, 45.500000, 45.700000,
    45.650000, 45.650000, 45.516667, 45.516667, 45.380000,
    46.350000, 46.791000, 45.433000, 45.366700, 45.76667, 45.65000, 45.36667, 45.48333, 45.5
  ),
  lon = c(
    -73.740833, -73.580000, -73.930000, -73.850000, -73.500000,
    -73.733300, -73.883300, -73.783333, -73.416667, -73.430000,
    -72.567000, -71.393000, -71.683000, -71.833300, -73.36667, -73.30000, -71.53333, -71.950000, -73.4
  )
)
 
# 2. Define the centroids
centroids <- data.frame(
  city = c("Montreal", "Laval", "Longueuil", "Trois-Rivieres", "Quebec", "Sherbrooke"),
  lat  = c(45.508888, 45.600000, 45.536945, 46.343239, 46.813878, 45.404476),
  lon  = c(-73.561668, -73.733000, -73.510712, -72.543283, -71.207981, -71.888351)
)

# 3. Compute distance matrix and assign nearest city
dist_mat <- distm(
  stations[, c("lon", "lat")],
  centroids[, c("lon", "lat")],
  fun = geosphere::distHaversine
)
stations$nearest <- centroids$city[apply(dist_mat, 1, which.min)]

stations_and_location<- 
stations_df |> 
  left_join(stations, by=c("alias"="station"))|> 
  mutate(mrc= case_when(
    nearest == "Laval" ~ 65,
    nearest == "Longueuil" ~ 58,
    nearest == "Montreal" ~ 66,
    nearest == "Quebec" ~ 23,
    nearest == "Sherbrooke" ~ 43,
    nearest == "Trois-Rivieres" ~ 371, T~ NA_real_
  ))


stations_and_location|> 
  filter(!alias %in% c("lave_dorothee", "ste_genevieve"))|>
  knitr::kable(caption = "Updated Stations with Nearest Cities")

Updated Stations with Nearest Cities

alias	station_id	start	end	station_name	lat	lon	nearest	mrc
trudeau	30165	2002	2025	MONTREAL/PIERRE ELLIOTT TRUDEAU INTL	45.47056	-73.74083	Laval	65
mctavish	10761	1994	2025	MCTAVISH	45.50000	-73.58000	Montreal	66
ste_anne	10873	1993	2025	STE-ANNE-DE-BELLEVUE 1	45.43000	-73.93000	Laval	65
rdp	5441	1973	2025	RIVIERE DES PRAIRIES	45.70000	-73.50000	Longueuil	58
lave_auteuil	5313	1983	2023	AUTEUIL	45.65000	-73.73330	Laval	65
ste_therese	5285	1961	2023	STE THERESE OUEST	45.65000	-73.88330	Laval	65
st_hubert	5490	1928	2015	MONTREAL/ST-HUBERT A	45.51667	-73.41667	Longueuil	58
la_prairie	5389	1963	2025	LAPRAIRIE	45.38000	-73.43000	Montreal	66
vercheres	5564	1963	2025	VERCHERES	45.76667	-73.36667	Longueuil	58
st_amable	5447	1980	2023	ST AMABLE	45.65000	-73.30000	Longueuil	58
trois_rivieres	5201	1974	2023	TROIS RIVIERES AQUEDUC	46.35000	-72.56700	Trois-Rivieres	371
quebec_airport	26892	1992	2025	QUEBEC/JEAN LESAGE INTL	46.79100	-71.39300	Quebec	23
sherbrooke_air	5530	1962	2016	SHERBROOKE A	45.43300	-71.68300	Sherbrooke	43
lennoxville	5397	1888	2025	LENNOXVILLE	45.36670	-71.83330	Sherbrooke	43
vercheres	5564	1963	2025	VERCHERES	45.76667	-73.36667	Longueuil	58
st_amable	5447	1980	2023	ST AMABLE	45.65000	-73.30000	Longueuil	58
sawyerville_nord	5526	1961	2025	SAWYERVILLE NORD	45.36667	-71.53333	Sherbrooke	43
bromptonville	5327	1957	2025	BROMPTONVILLE	45.48333	-71.95000	Sherbrooke	43
st_hubert2	48374	2009	2025	MONTREAL/ST-HUBERT	45.50000	-73.40000	Longueuil	58

STE GENEVIEVE and STE DOROTHEE were discarded because they did not coer the study period.

There are missing (“M”) values not well labelled as such.

For each F1 race from 1978:

• took one of the 10 stations listed above
• selected the period between May and September of each year
• for different measurements, we calculated the median and the mean (not sure if this is useful if each observation is a daily measurement) of precipitations, maximum and minimum temperature, snow, etc.

#weather_dl( 30165, start="1978-10-01", end="1978-10-15")
#skip givin NA if no data available for a year
#skip if there is no data for a given year

#vignette("flags", package = "weathercan")
get_window <- function(st_id, alias) {
  # Info de la estación
  station_info <- stations_df %>% filter(station_id == st_id)
  start_avail <- as_date(station_info$start)
  end_avail   <- as_date(station_info$end)

  races %>%
    #filter(!is.na(race_date)) %>%
    rowwise() %>%
    filter(year > 1999, year < 2023)%>% # The extra parenthesis was here
    mutate(
      range_start = make_date(year, 5, 1),#race_date - days(14),
      range_end   = make_date(year, 9, 30),#race_date+  days(14),
      data = list(
        tryCatch({
          df <- weather_dl(station_id = st_id,
                           start = range_start,
                           end   = range_end,
                           interval = "day") %>%
            group_by(date) %>%
            summarise(
              across(
                any_of(c(
                  "pressure", "rel_hum", "precip_amt",
                  "min_temp", "max_temp", "total_precip", "total_rain", "total_snow",
                  "temp", "temp_dew", "wind_chill", "wind_spd"
                )),
                list(
                  mean   = ~ mean(.x, na.rm = TRUE),
                  median = ~ median(.x, na.rm = TRUE)
                ),
                .names = "{.col}_{.fn}"
              )
            )
          df
        }, error = function(e) tibble())
      )
    ) %>%
    ungroup() %>%
    unnest(data) %>%
    mutate(station = alias)%>%
    relocate(station, date, race_date)
}
#2004-06-06
#2004-06-20
## 30165 10761 10873  3758  5441  5313  5285  5460  5490  5389  5201 26892  5530  5528  5397
## There are no data for station 5528, in this time range (2004-06-06 to 2004-06-20), for this interval (day), 
# so we skip it
test<- 
 weather_dl(station_id = 5441,#5460, #
           start = make_date(2022, 5, 1),#"1978-10-01",
           end   = make_date(2022, 9, 30),#"1978-10-15",
           interval = "day") |>
   group_by(date) |> 
  summarise(
    across(
      any_of(c(
        "pressure", "rel_hum", "precip_amt",
        "min_temp", "max_temp", "total_precip", "total_rain", "total_snow",
        "temp", "temp_dew", "wind_chill", "wind_spd"
      )),
      list(
        mean   = ~ mean(.x, na.rm = TRUE),
        median = ~ median(.x, na.rm = TRUE)
      ),
      .names = "{.col}_{.fn}"
    )
  )         

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

wx_all <- map2_dfr(setdiff(stations_df$station_id,c(3758, 5460)), setdiff(stations_df$alias,c("lave_dorothee", "ste_genevieve")), get_window) 

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 30165, in this time range (2000-05-01 to 2000-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/PIE…      30165 702S006     71183 WTQ    45.5 -73.7  32.1 Etc/…
2 QC    MONTREAL/PIE…      30165 702S006     71183 WTQ    45.5 -73.7  32.1 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 30165, in this time range (2001-05-01 to 2001-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/PIE…      30165 702S006     71183 WTQ    45.5 -73.7  32.1 Etc/…
2 QC    MONTREAL/PIE…      30165 702S006     71183 WTQ    45.5 -73.7  32.1 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 30165, in this time range (2002-05-01 to 2002-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/PIE…      30165 702S006     71183 WTQ    45.5 -73.7  32.1 Etc/…
2 QC    MONTREAL/PIE…      30165 702S006     71183 WTQ    45.5 -73.7  32.1 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 30165

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 30165

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 30165

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 30165

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 30165

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 30165

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 30165

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 30165

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 30165

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 30165

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 30165

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 30165

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 30165

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 30165

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 30165

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 30165

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10761

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 10873

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5313, in this time range (2017-05-01 to 2017-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    AUTEUIL            5313 7020392        NA <NA>   45.6 -73.7    53 Etc/G…
2 QC    AUTEUIL            5313 7020392        NA <NA>   45.6 -73.7    53 Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5313, in this time range (2018-05-01 to 2018-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    AUTEUIL            5313 7020392        NA <NA>   45.6 -73.7    53 Etc/G…
2 QC    AUTEUIL            5313 7020392        NA <NA>   45.6 -73.7    53 Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5313, in this time range (2019-05-01 to 2019-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    AUTEUIL            5313 7020392        NA <NA>   45.6 -73.7    53 Etc/G…
2 QC    AUTEUIL            5313 7020392        NA <NA>   45.6 -73.7    53 Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5313, in this time range (2020-05-01 to 2020-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    AUTEUIL            5313 7020392        NA <NA>   45.6 -73.7    53 Etc/G…
2 QC    AUTEUIL            5313 7020392        NA <NA>   45.6 -73.7    53 Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5313, in this time range (2021-05-01 to 2021-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    AUTEUIL            5313 7020392        NA <NA>   45.6 -73.7    53 Etc/G…
2 QC    AUTEUIL            5313 7020392        NA <NA>   45.6 -73.7    53 Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5313, in this time range (2022-05-01 to 2022-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    AUTEUIL            5313 7020392        NA <NA>   45.6 -73.7    53 Etc/G…
2 QC    AUTEUIL            5313 7020392        NA <NA>   45.6 -73.7    53 Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5285, in this time range (2013-05-01 to 2013-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    STE THERESE …       5285 7017755        NA <NA>   45.6 -73.9    61 Etc/…
2 QC    STE THERESE …       5285 7017755        NA <NA>   45.6 -73.9    61 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5285, in this time range (2014-05-01 to 2014-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    STE THERESE …       5285 7017755        NA <NA>   45.6 -73.9    61 Etc/…
2 QC    STE THERESE …       5285 7017755        NA <NA>   45.6 -73.9    61 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5285, in this time range (2015-05-01 to 2015-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    STE THERESE …       5285 7017755        NA <NA>   45.6 -73.9    61 Etc/…
2 QC    STE THERESE …       5285 7017755        NA <NA>   45.6 -73.9    61 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5285, in this time range (2017-05-01 to 2017-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    STE THERESE …       5285 7017755        NA <NA>   45.6 -73.9    61 Etc/…
2 QC    STE THERESE …       5285 7017755        NA <NA>   45.6 -73.9    61 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5285, in this time range (2018-05-01 to 2018-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    STE THERESE …       5285 7017755        NA <NA>   45.6 -73.9    61 Etc/…
2 QC    STE THERESE …       5285 7017755        NA <NA>   45.6 -73.9    61 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5285, in this time range (2019-05-01 to 2019-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    STE THERESE …       5285 7017755        NA <NA>   45.6 -73.9    61 Etc/…
2 QC    STE THERESE …       5285 7017755        NA <NA>   45.6 -73.9    61 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5285, in this time range (2020-05-01 to 2020-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    STE THERESE …       5285 7017755        NA <NA>   45.6 -73.9    61 Etc/…
2 QC    STE THERESE …       5285 7017755        NA <NA>   45.6 -73.9    61 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5285, in this time range (2021-05-01 to 2021-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    STE THERESE …       5285 7017755        NA <NA>   45.6 -73.9    61 Etc/…
2 QC    STE THERESE …       5285 7017755        NA <NA>   45.6 -73.9    61 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5285, in this time range (2022-05-01 to 2022-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    STE THERESE …       5285 7017755        NA <NA>   45.6 -73.9    61 Etc/…
2 QC    STE THERESE …       5285 7017755        NA <NA>   45.6 -73.9    61 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5490, in this time range (2005-05-01 to 2005-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
3 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5490, in this time range (2006-05-01 to 2006-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
3 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5490, in this time range (2007-05-01 to 2007-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
3 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5490, in this time range (2008-05-01 to 2008-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
3 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5490, in this time range (2012-05-01 to 2012-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
3 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5490, in this time range (2013-05-01 to 2013-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
3 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5490, in this time range (2014-05-01 to 2014-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
3 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5490, in this time range (2015-05-01 to 2015-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
3 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5490, in this time range (2016-05-01 to 2016-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
3 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5490, in this time range (2017-05-01 to 2017-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
3 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5490, in this time range (2018-05-01 to 2018-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
3 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5490, in this time range (2019-05-01 to 2019-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
3 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5490, in this time range (2020-05-01 to 2020-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
3 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5490, in this time range (2021-05-01 to 2021-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
3 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5490, in this time range (2022-05-01 to 2022-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
3 QC    MONTREAL/ST-…       5490 7027320     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5447, in this time range (2017-05-01 to 2017-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    ST AMABLE          5447 7026818        NA <NA>   45.7 -73.3  41.1 Etc/G…
2 QC    ST AMABLE          5447 7026818        NA <NA>   45.7 -73.3  41.1 Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5447, in this time range (2018-05-01 to 2018-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    ST AMABLE          5447 7026818        NA <NA>   45.7 -73.3  41.1 Etc/G…
2 QC    ST AMABLE          5447 7026818        NA <NA>   45.7 -73.3  41.1 Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5447, in this time range (2019-05-01 to 2019-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    ST AMABLE          5447 7026818        NA <NA>   45.7 -73.3  41.1 Etc/G…
2 QC    ST AMABLE          5447 7026818        NA <NA>   45.7 -73.3  41.1 Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5447, in this time range (2020-05-01 to 2020-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    ST AMABLE          5447 7026818        NA <NA>   45.7 -73.3  41.1 Etc/G…
2 QC    ST AMABLE          5447 7026818        NA <NA>   45.7 -73.3  41.1 Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5447, in this time range (2021-05-01 to 2021-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    ST AMABLE          5447 7026818        NA <NA>   45.7 -73.3  41.1 Etc/G…
2 QC    ST AMABLE          5447 7026818        NA <NA>   45.7 -73.3  41.1 Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5447, in this time range (2022-05-01 to 2022-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    ST AMABLE          5447 7026818        NA <NA>   45.7 -73.3  41.1 Etc/G…
2 QC    ST AMABLE          5447 7026818        NA <NA>   45.7 -73.3  41.1 Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5201, in this time range (2010-05-01 to 2010-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
2 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5201, in this time range (2011-05-01 to 2011-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
2 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5201, in this time range (2012-05-01 to 2012-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
2 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5201, in this time range (2013-05-01 to 2013-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
2 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5201, in this time range (2014-05-01 to 2014-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
2 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5201, in this time range (2015-05-01 to 2015-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
2 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5201, in this time range (2017-05-01 to 2017-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
2 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5201, in this time range (2018-05-01 to 2018-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
2 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5201, in this time range (2019-05-01 to 2019-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
2 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5201, in this time range (2020-05-01 to 2020-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
2 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5201, in this time range (2021-05-01 to 2021-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
2 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5201, in this time range (2022-05-01 to 2022-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
2 QC    TROIS RIVIER…       5201 701HE63        NA <NA>   46.4 -72.6  54.9 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 26892

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 26892

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 26892

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5530

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5530, in this time range (2011-05-01 to 2011-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
2 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
3 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5530, in this time range (2017-05-01 to 2017-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
2 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
3 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5530, in this time range (2018-05-01 to 2018-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
2 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
3 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5530, in this time range (2019-05-01 to 2019-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
2 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
3 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5530, in this time range (2020-05-01 to 2020-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
2 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
3 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5530, in this time range (2021-05-01 to 2021-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
2 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
3 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 5530, in this time range (2022-05-01 to 2022-09-30), for this interval (day), 
Available Station Data:
# A tibble: 3 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr> 
1 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
2 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
3 QC    SHERBROOKE A       5530 7028124     71610 YSC    45.4 -71.7  241. Etc/G…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 5397

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 48374, in this time range (2000-05-01 to 2000-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 48374, in this time range (2001-05-01 to 2001-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 48374, in this time range (2002-05-01 to 2002-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 48374, in this time range (2003-05-01 to 2003-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 48374, in this time range (2004-05-01 to 2004-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 48374, in this time range (2005-05-01 to 2005-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 48374, in this time range (2006-05-01 to 2006-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 48374, in this time range (2007-05-01 to 2007-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 48374, in this time range (2008-05-01 to 2008-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

There are no data for station 48374, in this time range (2009-05-01 to 2009-09-30), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name  station_id climate_id WMO_id TC_id   lat   lon  elev tz   
  <chr> <chr>              <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
2 QC    MONTREAL/ST-…      48374 7027329     71371 YHU    45.5 -73.4  27.4 Etc/…
# ℹ 7 more variables: interval <chr>, start <dbl>, end <dbl>, normals <lgl>,
#   normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 48374

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 48374

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 48374

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 48374

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 48374

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 48374

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 48374

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 48374

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

Some variables have non-numeric values (spd_max_gust), for stations: 48374

  Replaced all non-numeric entries with NA. Use 'string_as = NULL' to keep as characters (see ?weather_dl).

The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available
The stations data frame hasn't been updated in over 4 weeks. Consider running `stations_dl()` to check for updates and make sure you have the most recent stations list available

wx_all <- wx_all%>% 
    mutate(
    # define an indicator that is 1 if race_date ∈ [or3, or3 + 3 days], 0 otherwise
    study_window = as.integer(dplyr::between(date, race_date - days(35), race_date + days(35)))
  )

#Select the relevant variables
wx_all_subset <- wx_all %>%
  dplyr::select(station, min_temp_mean, min_temp_median, max_temp_mean, max_temp_median, 
         total_precip_mean, total_precip_median, total_rain_mean, 
         total_rain_median, total_snow_mean, total_snow_median)

# Count missing values for station and variables
missing_counts <- wx_all_subset %>%
  pivot_longer(cols = -station, names_to = "variable", values_to = "value") %>%
  group_by(station, variable) %>%
  summarize(missing_count = sum(is.na(value) | is.nan(value)),n=n(), perc= missing_count/n, .groups = "drop")

# Graphic with facet_wrap by station
missing_counts|> 
  filter(variable %in% c(paste0( c("max_temp", "min_temp"), "_mean"), paste0( c("total_precip", "total_rain", "total_snow"), "_median")))|> 
ggplot(aes(x = variable, y = perc, fill = variable)) +
  geom_bar(stat = "identity") +
  facet_wrap(~ station, ncol = 4) +
  labs(x = "Variable", y = "Proportion of missing values") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        legend.position = "none")

Proportion of missing values by station and variable

Due to the high proportion of missing values, the variables total_rain_mean, total_rain_median, total_snow_mean, and total_snow_median were removed from the analysis. Likewise, the monitoring sites La Prairie (Montreal), Ste-Anne (Laval), and Trudeau (Laval) might be excluded from further analysis.

Here it is a glossary of the metrics selected³.

Variable	Description	Unit
max_temp	Daily maximum air temperature: the highest dry-bulb temperature recorded during the 24-hour observation period.	degrees Celsius (°C) ([Climate Data][1])
min_temp	Daily minimum air temperature: the lowest dry-bulb temperature recorded during the 24-hour observation period.	degrees Celsius (°C) ([Climate Data][1])
tot_precip	Daily total precipitation: sum of the water equivalent of all liquid and solid precipitation (rain, snow, hail, etc.) accumulated over the day.	millimetres (mm) ([Climate Data][1])
tot_precip_lag2	Two-day-lagged total precipitation: the value of tot_precip shifted backward by two days, useful for capturing antecedent moisture conditions in analyses.	millimetres (mm) ([Climate Data][1])

wx_summary_filt2002_22_all<- 
wx_all %>%
  filter(date>"1999-12-31", date<"2023-01-01")|> 
  select(station, date, race_date, year, min_temp_mean, min_temp_median, max_temp_mean, max_temp_median, 
         total_precip_mean, total_precip_median) %>%
  left_join(stations_and_location, by = c("station"="alias"), multiple="first") %>%
  group_by(date, nearest, mrc) %>%
  summarise(across(c("min_temp_mean", "min_temp_median", "max_temp_mean", "max_temp_median", "total_precip_mean", "total_precip_median"), ~ mean(.x, na.rm=T))) |> 
  ungroup()

`summarise()` has grouped output by 'date', 'nearest'. You can override using
the `.groups` argument.

wx_summary_filt2002_22_all %>%
  ungroup() |> 
  select(
    nearest,
    min_temp_mean, min_temp_median,
    max_temp_mean, max_temp_median,
    total_precip_mean, total_precip_median
  ) %>%
  pivot_longer(
    cols = -nearest,
    names_to = "variable",
    values_to = "value"
  ) %>%
  group_by(nearest, variable) %>%
  summarise(
    missing_count = sum(is.na(value) | is.nan(value)),
    n             = n(),
    perc_missing  = scales::percent(missing_count / n, accuracy=.1),
    .groups       = "drop"
  ) %>%
  arrange(nearest, variable) -> missing_summary

missing_summary|> 
  head(20) %>%
  knitr::kable(caption = "Sample of missing values by station and variable")

Sample of missing values by station and variable

nearest	variable	missing_count	n	perc_missing
Laval	max_temp_mean	6	3519	0.2%
Laval	max_temp_median	6	3519	0.2%
Laval	min_temp_mean	7	3519	0.2%
Laval	min_temp_median	7	3519	0.2%
Laval	total_precip_mean	3	3519	0.1%
Laval	total_precip_median	3	3519	0.1%
Longueuil	max_temp_mean	3	3519	0.1%
Longueuil	max_temp_median	3	3519	0.1%
Longueuil	min_temp_mean	2	3519	0.1%
Longueuil	min_temp_median	2	3519	0.1%
Longueuil	total_precip_mean	2	3519	0.1%
Longueuil	total_precip_median	2	3519	0.1%
Montreal	max_temp_mean	18	3519	0.5%
Montreal	max_temp_median	18	3519	0.5%
Montreal	min_temp_mean	12	3519	0.3%
Montreal	min_temp_median	12	3519	0.3%
Montreal	total_precip_mean	47	3519	1.3%
Montreal	total_precip_median	47	3519	1.3%
Quebec	max_temp_mean	24	3519	0.7%
Quebec	max_temp_median	24	3519	0.7%

wx_summary_filt2002_22_all |> 
  head(20) %>%
  knitr::kable(caption = "Sample of average values by station and date", digits=1, col.names = c("Date", "Nearest City", "MRC", "Min Temp (M°C)", "Min Temp (Mdn°C)", "Max Temp (M°C)", "Max Temp(Mdn°C)", "Precipitation (M mm)", "Precipitation (Mdn mm)"))

Sample of average values by station and date

Date	Nearest City	MRC	Min Temp (M°C)	Min Temp (Mdn°C)	Max Temp (M°C)	Max Temp(Mdn°C)	Precipitation (M mm)	Precipitation (Mdn mm)
2000-05-01	Laval	65	3.0	3.0	14.6	14.6	1.4	1.4
2000-05-01	Longueuil	58	0.8	0.8	14.7	14.7	1.6	1.6
2000-05-01	Montreal	66	4.0	4.0	14.7	14.7	2.5	2.5
2000-05-01	Quebec	23	-1.4	-1.4	13.3	13.3	4.0	4.0
2000-05-01	Sherbrooke	43	-3.3	-3.3	13.8	13.8	2.5	2.5
2000-05-01	Trois-Rivieres	371	-2.0	-2.0	13.0	13.0	2.7	2.7
2000-05-02	Laval	65	3.6	3.6	15.1	15.1	0.3	0.3
2000-05-02	Longueuil	58	3.5	3.5	14.8	14.8	0.0	0.0
2000-05-02	Montreal	66	5.3	5.3	14.6	14.6	0.5	0.5
2000-05-02	Quebec	23	1.1	1.1	12.8	12.8	0.0	0.0
2000-05-02	Sherbrooke	43	0.2	0.2	11.0	11.0	0.5	0.5
2000-05-02	Trois-Rivieres	371	2.0	2.0	13.0	13.0	0.0	0.0
2000-05-03	Laval	65	0.5	0.5	19.3	19.3	0.0	0.0
2000-05-03	Longueuil	58	-0.9	-0.9	19.5	19.5	0.0	0.0
2000-05-03	Montreal	66	2.5	2.5	18.4	18.4	0.0	0.0
2000-05-03	Quebec	23	-1.0	-1.0	16.5	16.5	0.0	0.0
2000-05-03	Sherbrooke	43	-2.7	-2.7	17.1	17.1	0.0	0.0
2000-05-03	Trois-Rivieres	371	-2.5	-2.5	17.0	17.0	0.0	0.0
2000-05-04	Laval	65	4.3	4.3	24.6	24.6	0.0	0.0
2000-05-04	Longueuil	58	3.7	3.7	24.6	24.6	0.6	0.6

summary_vars <- c("min_temp_mean", "min_temp_median", "max_temp_mean",
                  "max_temp_median", "total_precip_mean", "total_precip_median")

# Realizar el resumen
summary_stats <- wx_summary_filt2002_22_all %>%
  group_by(nearest) %>%
  summarise(
    across(.cols = all_of(summary_vars),
           .fns = list(
             mean_sd = ~ sprintf("%.1f (%.1f)", mean(.x, na.rm = TRUE), sd(.x, na.rm = TRUE)),
             median_iqr = ~ sprintf("%.1f [%.1f, %.1f]",
                                    median(.x, na.rm = TRUE),
                                    quantile(.x, probs = 0.25, na.rm = TRUE),
                                    quantile(.x, probs = 0.75, na.rm = TRUE)),
             p5_p95_range = ~ sprintf("%.1f - %.1f",
                                      quantile(.x, probs = 0.05, na.rm = TRUE),
                                      quantile(.x, probs = 0.95, na.rm = TRUE))
           ),
           .names = "{.col}_{.fn}" # Naming convention for the new columns
    )
  )

summary_stats_long <- summary_stats %>%
  pivot_longer(
    cols = -nearest, # Todas las columnas excepto 'nearest'
    names_to = c("variable_original", "statistic_type"), # Crea dos nuevas columnas para los nombres
    names_sep = "_(?=[^_]*$)", # Separa por el último '_' para obtener la variable y el tipo de estadística
    values_to = "value" # La columna donde irán los valores formateados
  ) %>%
  # Opcional: Reordenar y limpiar los nombres de las variables/estadísticas
  mutate(
    variable_original = gsub("_(mean|median)$", "", variable_original), # Quitar _mean o _median del nombre de la variable
    statistic_type = case_when(
      statistic_type == "mean_sd" ~ "Mean (SD)",
      statistic_type == "median_iqr" ~ "Median [Q1, Q3]",
      statistic_type == "p5_p95_range" ~ "Range [P5, P95]",
      TRUE ~ statistic_type # Por si acaso hay otros tipos que no queremos renombrar
    )
  ) %>%
  # Opcional: Reordenar las columnas para mejor lectura
  relocate(nearest, variable_original, statistic_type, value) %>%
  arrange(nearest, variable_original, statistic_type) |> 
  mutate(variable_original= gsub("_p5_p95","",variable_original)) |> 
  mutate(statistic_type= gsub("sd","mean sd",statistic_type)) |> 
  mutate(statistic_type= gsub("range","90% range",statistic_type))

summary_stats_long |> 
  knitr::kable(caption = "Summary statistics for weather variables by nearest city",
               col.names = c("Nearest City", "Variable", "Statistic Type", "Value"),
               digits = 1, format.args = list(big.mark = ",")) %>%
  kableExtra::kable_classic() |> 
  kableExtra::scroll_box(width = "100%", height = "400px")

Summary statistics for weather variables by nearest city

Nearest City	Variable	Statistic Type	Value
Laval	max_temp_mean	iqr	24.2 [20.2, 27.2]
Laval	max_temp_mean	mean sd	23.5 (5.1)
Laval	max_temp_mean	90% range	14.3 - 30.9
Laval	max_temp_median	iqr	24.2 [20.2, 27.2]
Laval	max_temp_median	mean sd	23.5 (5.1)
Laval	max_temp_median	90% range	14.3 - 30.9
Laval	min_temp_mean	iqr	13.2 [9.6, 16.4]
Laval	min_temp_mean	mean sd	12.8 (4.8)
Laval	min_temp_mean	90% range	4.4 - 19.9
Laval	min_temp_median	iqr	13.2 [9.6, 16.4]
Laval	min_temp_median	mean sd	12.8 (4.8)
Laval	min_temp_median	90% range	4.4 - 19.9
Laval	total_precip_mean	iqr	0.1 [0.0, 2.4]
Laval	total_precip_mean	mean sd	2.9 (6.6)
Laval	total_precip_mean	90% range	0.0 - 15.4
Laval	total_precip_median	iqr	0.1 [0.0, 2.4]
Laval	total_precip_median	mean sd	2.9 (6.6)
Laval	total_precip_median	90% range	0.0 - 15.4
Longueuil	max_temp_mean	iqr	24.4 [20.4, 27.3]
Longueuil	max_temp_mean	mean sd	23.7 (5.1)
Longueuil	max_temp_mean	90% range	14.5 - 31.1
Longueuil	max_temp_median	iqr	24.4 [20.4, 27.3]
Longueuil	max_temp_median	mean sd	23.7 (5.1)
Longueuil	max_temp_median	90% range	14.5 - 31.1
Longueuil	min_temp_mean	iqr	13.1 [9.4, 16.2]
Longueuil	min_temp_mean	mean sd	12.7 (4.8)
Longueuil	min_temp_mean	90% range	4.0 - 19.9
Longueuil	min_temp_median	iqr	13.1 [9.4, 16.2]
Longueuil	min_temp_median	mean sd	12.7 (4.8)
Longueuil	min_temp_median	90% range	4.0 - 19.9
Longueuil	total_precip_mean	iqr	0.1 [0.0, 2.9]
Longueuil	total_precip_mean	mean sd	3.1 (6.8)
Longueuil	total_precip_mean	90% range	0.0 - 16.9
Longueuil	total_precip_median	iqr	0.1 [0.0, 2.9]
Longueuil	total_precip_median	mean sd	3.1 (6.8)
Longueuil	total_precip_median	90% range	0.0 - 16.9
Montreal	max_temp_mean	iqr	24.3 [20.4, 27.4]
Montreal	max_temp_mean	mean sd	23.6 (5.1)
Montreal	max_temp_mean	90% range	14.6 - 31.1
Montreal	max_temp_median	iqr	24.3 [20.4, 27.4]
Montreal	max_temp_median	mean sd	23.6 (5.1)
Montreal	max_temp_median	90% range	14.6 - 31.1
Montreal	min_temp_mean	iqr	14.0 [10.3, 17.0]
Montreal	min_temp_mean	mean sd	13.5 (4.7)
Montreal	min_temp_mean	90% range	5.1 - 20.4
Montreal	min_temp_median	iqr	14.0 [10.3, 17.0]
Montreal	min_temp_median	mean sd	13.5 (4.7)
Montreal	min_temp_median	90% range	5.1 - 20.4
Montreal	total_precip_mean	iqr	0.0 [0.0, 2.8]
Montreal	total_precip_mean	mean sd	3.2 (7.2)
Montreal	total_precip_mean	90% range	0.0 - 17.3
Montreal	total_precip_median	iqr	0.0 [0.0, 2.8]
Montreal	total_precip_median	mean sd	3.2 (7.2)
Montreal	total_precip_median	90% range	0.0 - 17.3
Quebec	max_temp_mean	iqr	22.3 [18.2, 25.6]
Quebec	max_temp_mean	mean sd	21.7 (5.3)
Quebec	max_temp_mean	90% range	12.2 - 29.4
Quebec	max_temp_median	iqr	22.3 [18.2, 25.6]
Quebec	max_temp_median	mean sd	21.7 (5.3)
Quebec	max_temp_median	90% range	12.2 - 29.4
Quebec	min_temp_mean	iqr	10.6 [7.1, 13.7]
Quebec	min_temp_mean	mean sd	10.2 (4.7)
Quebec	min_temp_mean	90% range	1.8 - 17.4
Quebec	min_temp_median	iqr	10.6 [7.1, 13.7]
Quebec	min_temp_median	mean sd	10.2 (4.7)
Quebec	min_temp_median	90% range	1.8 - 17.4
Quebec	total_precip_mean	iqr	0.0 [0.0, 3.1]
Quebec	total_precip_mean	mean sd	3.5 (8.0)
Quebec	total_precip_mean	90% range	0.0 - 18.7
Quebec	total_precip_median	iqr	0.0 [0.0, 3.1]
Quebec	total_precip_median	mean sd	3.5 (8.0)
Quebec	total_precip_median	90% range	0.0 - 18.7
Sherbrooke	max_temp_mean	iqr	23.1 [19.2, 26.2]
Sherbrooke	max_temp_mean	mean sd	22.5 (5.1)
Sherbrooke	max_temp_mean	90% range	13.1 - 29.9
Sherbrooke	max_temp_median	iqr	23.1 [19.2, 26.2]
Sherbrooke	max_temp_median	mean sd	22.5 (5.1)
Sherbrooke	max_temp_median	90% range	13.1 - 29.9
Sherbrooke	min_temp_mean	iqr	10.8 [7.0, 14.2]
Sherbrooke	min_temp_mean	mean sd	10.4 (5.1)
Sherbrooke	min_temp_mean	90% range	0.9 - 18.0
Sherbrooke	min_temp_median	iqr	10.8 [7.0, 14.2]
Sherbrooke	min_temp_median	mean sd	10.4 (5.1)
Sherbrooke	min_temp_median	90% range	0.9 - 18.0
Sherbrooke	total_precip_mean	iqr	0.3 [0.0, 3.8]
Sherbrooke	total_precip_mean	mean sd	3.5 (6.8)
Sherbrooke	total_precip_mean	90% range	0.0 - 17.5
Sherbrooke	total_precip_median	iqr	0.3 [0.0, 3.8]
Sherbrooke	total_precip_median	mean sd	3.5 (6.8)
Sherbrooke	total_precip_median	90% range	0.0 - 17.5
Trois-Rivieres	max_temp_mean	iqr	23.0 [19.0, 26.0]
Trois-Rivieres	max_temp_mean	mean sd	22.5 (5.1)
Trois-Rivieres	max_temp_mean	90% range	13.0 - 30.0
Trois-Rivieres	max_temp_median	iqr	23.0 [19.0, 26.0]
Trois-Rivieres	max_temp_median	mean sd	22.5 (5.1)
Trois-Rivieres	max_temp_median	90% range	13.0 - 30.0
Trois-Rivieres	min_temp_mean	iqr	12.0 [8.0, 15.0]
Trois-Rivieres	min_temp_mean	mean sd	11.4 (5.2)
Trois-Rivieres	min_temp_mean	90% range	2.0 - 19.5
Trois-Rivieres	min_temp_median	iqr	12.0 [8.0, 15.0]
Trois-Rivieres	min_temp_median	mean sd	11.4 (5.2)
Trois-Rivieres	min_temp_median	90% range	2.0 - 19.5
Trois-Rivieres	total_precip_mean	iqr	0.0 [0.0, 3.2]
Trois-Rivieres	total_precip_mean	mean sd	3.3 (7.2)
Trois-Rivieres	total_precip_mean	90% range	0.0 - 17.7
Trois-Rivieres	total_precip_median	iqr	0.0 [0.0, 3.2]
Trois-Rivieres	total_precip_median	mean sd	3.3 (7.2)
Trois-Rivieres	total_precip_median	90% range	0.0 - 17.7

We chose to maintain the tot_precip variable as the average of the median precipitation through stations. The rest of variables are the average across stations.

Collisions database

We imported a Stata-formatted dataset named Allcollisions2000_22.summer.2.dta, storing it as the collisions data frame. This comprehensive dataset chronicles traffic collisions in Quebec from 2000 to 2022, with a specific focus on the summer months (May to September, roughly late spring to early autumn). It includes data from several key cities and census divisions (MRCs), such as Trois-Rivières (MRC 371) and Québec (MRC 23), which serve as control areas, alongside Laval (MRC 65), Montréal (MRC 66), Longueuil (MRC 58), and Sherbrooke (MRC 43), the latter also may act as a control.

collisions<- 
rio::import(paste0(getwd(),"/_data/Allcollisions2000_22.summer.2.dta"))

#  The SAAQ dataset is considered complete for fatal collisions, but mild-injury counts can be under-reported.
# • Alcohol and speed flags reflect the police officer’s assessment; they are not mutually exclusive.
glimpse(collisions, width=80)

Rows: 17,520
Columns: 12
$ mrc              <dbl> 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 2…
$ stata_date       <date> 2000-05-01, 2000-05-02, 2000-05-03, 2000-05-04, 2000…
$ nb_collisions    <dbl> 21, 19, 24, 23, 21, 11, 28, 22, 25, 26, 23, 27, 16, 1…
$ alcohol          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ velocidad        <dbl> 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0,…
$ nb_mort_c        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,…
$ nb_blese_grave_c <dbl> 0, 0, 2, 0, 0, 1, 1, 2, 0, 2, 0, 0, 0, 0, 0, 1, 1, 0,…
$ nb_blese_leger_c <dbl> 3, 7, 2, 6, 8, 3, 9, 3, 4, 6, 4, 11, 5, 3, 3, 8, 2, 9…
$ nb_vehicules     <dbl> 38, 36, 47, 46, 43, 22, 52, 43, 48, 48, 47, 51, 28, 2…
$ year             <dbl> 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000,…
$ month            <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,…
$ day_of_week      <dbl> 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5,…

Trois-Rivières (MRC 371) is absent from the database. That leaves us with Quebec (MRC 23), and possibly Sherbrooke (MRC 43) as the control cities.

Weimported the offset data form google sheets containing vehicle/license holder statistics by year and city. then, we joined with weather data and defined treatment/control grou based on MRC codes, createdmultiple exposure widows around race dates. Posteriorly, we added temporal variables (ISO week, year of day), we also generated treatment indicator (D). Lastly, for data quality assessment, we checked for duplicates in the dataset, identified missing dates, generated diagnostic tables showing row count discrepancies by MRC/year and specific missing dates in the collision database.

#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
invisible("Add offset")

labels <- c(
  "License holders by sex and age",
  "Vehicles in circulation by use and type",
  "Vehicles in circulation by use, type and vehicle age"
)

offsets_db<- rio::import("https://docs.google.com/spreadsheets/d/1znDbWVXO2A5uSKiVazQwFJKZcrrH-VZXnQsG-INBQ-w/gviz/tq?tqx=out:tsv&gid=819465529", skip=1, header=T) |> filter(MRC!="MRC")

offsets_db$ind <- c(rep("license_holders_sex_age",5), rep(NA, 2), rep("vehicles_use_type",5), rep(NA, 2), rep("vehicles_use_type_vehicle_age", 5))

offsets_db<- dplyr::select(offsets_db, EQUALS, MRC, ind, everything())|>
                filter(!is.na(`2000`))|>
                pivot_longer(cols= c("2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", 
"2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022"), names_to = "year")|>
  pivot_wider(names_from=ind, values_from=value)|> 
  mutate(year= as.numeric(year), EQUALS=ifelse(EQUALS=="Longueiuil", "Longueuil", EQUALS))
  


#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:

invisible("2025-06-25: missing data in weather variables")
# filter(wx_summary_filt2002_22_all, dplyr::between(date,as.Date("2006-09-24"), as.Date("2006-09-30")), nearest=="Sherbrooke")
# filter(wx_summary_filt2002_22_all, dplyr::between(date,as.Date("2019-05-01"), as.Date("2019-05-31")), nearest=="Longueuil")

wx_summary_filt2002_22_all$date_num <- unclass(wx_summary_filt2002_22_all$date)

collisions_weather<- 
collisions|>
  mutate(date_num= unclass(stata_date))|> 
  left_join(wx_summary_filt2002_22_all, by=c("date_num"="date_num", "mrc"="mrc"))|> 
  group_by(mrc, date)|> 
  arrange(mrc, date)|>  
  mutate(lag_2_prec_median= lag(total_precip_median, n=2))|> 
  ungroup()|>
  mutate(tr_contr=ifelse(mrc %in% c(371, 23), "control", "treatment"))|>
  mutate(tr_contr_sens=ifelse(mrc %in% c(371, 23, 43), "control", "treatment"))|>  #adding Sherbrook
  left_join(races, by="year")|>
  #2025-06-25. missing weather variables for Longueuil
  #select(year, date, mrc, nearest, date_num)|> #2025-06-25= 2006 43 2006-09-24 to 2006-09-30; 2019 58 2019-05-01 to 2019-05-31
  mutate(date_num= unclass(date))|>
  left_join(offsets_db, by= c("nearest"="EQUALS", "year"="year"))|>
  mutate(
    exposure          = case_when(
      date_num <= (unclass(race_date) - 2) &    # t0-2
      date_num <= (unclass(race_date) + 3) ~ 1, # t+3
      TRUE ~ 0
    ),
    exposure_alt7     = case_when(
      date_num <= (unclass(race_date) - 2) &    # t0-2
      date_num <= (unclass(race_date) + 7) ~ 1, # t+7
      TRUE ~ 0
    ),
    exposure_off      = case_when(
      date_num <= (unclass(race_date)) &        # t0
      date_num <= (unclass(race_date) + 3) ~ 1, # t+3
      TRUE ~ 0
    ),
    exposure_alt7_off = case_when(
      date_num <= (unclass(race_date)) &        # t0
      date_num <= (unclass(race_date) + 7) ~ 1, # t+7
      TRUE ~ 0
    )
  )|>
  mutate(
    week_iso            = isoweek(stata_date),
    year_f             = factor(year),
    mrc_f              = factor(mrc),
    #exposure          = factor(exposure, levels = c(0, 1)),
    iso_yday           = yday(stata_date),
    log_veh            = log(nb_vehicules + 1),  # adding 1 to avoid log(0)
    D                  = ifelse(tr_contr == "treatment" & exposure == 1, 1, 0),
    D_sens             = ifelse(tr_contr_sens == "treatment" & exposure == 1, 1, 0),
    D7                 = ifelse(tr_contr == "treatment" & exposure_alt7 == 1, 1, 0),
    D7_sens            = ifelse(tr_contr_sens == "treatment" & exposure_alt7 == 1, 1, 0),
    D_off              = ifelse(tr_contr == "treatment" & exposure_off == 1, 1, 0),
    D_sens_off         = ifelse(tr_contr_sens == "treatment" & exposure_off == 1, 1, 0),
    D7_off             = ifelse(tr_contr == "treatment" & exposure_alt7_off == 1, 1, 0),
    D7_sens_off        = ifelse(tr_contr_sens == "treatment" & exposure_alt7_off == 1, 1, 0)
  )|>
  left_join(offsets_db, by=c("year", "MRC", "license_holders_sex_age",
    "vehicles_use_type", "vehicles_use_type_vehicle_age"))

cat("Duplicates?\n")
collisions_weather |>
  mutate(date = as.Date(stata_date)) |>
  filter(month(date) %in% 5:9) |>
  count(mrc, year, date) |>
  filter(n > 1)

cat("How many rows the database should have approx?\n")
23*length(unique(collisions_weather$mrc))*as.numeric(difftime(as.Date("2000-09-30"),as.Date("2000-05-01"), "days"))

cat("What we have \n")
nrow(collisions_weather)

cat("What we should have \n")
weather_filled <- collisions_weather |>
  mutate(rn= row_number()) |> 
mutate(date = as.Date(iso_yday - 1,
                      origin = paste0(year, "-01-01"))) |>
    filter(month(date) %in% 5:9) |>
    group_by(mrc, year) |>
    complete(
        date = seq.Date(min(date), max(date), by = "day")
    ) |>
    ungroup()
nrow(weather_filled)

invisible("Filled database has more rows")
orig_db <- 
  collisions_weather |> group_by(mrc, year) |> summarise(n=n(),.groups="drop")
proc_db<- 
weather_filled |> group_by(mrc, year) |> summarise(n=n(),.groups="drop")

orig_db |> 
  left_join(proc_db, by=c("mrc", "year"))|> 
    filter(n.x!=n.y) |> 
  rename("original"="n.x", "processed"="n.y")|> 
   knitr::kable(caption="Number of by MRC and year",
               col.names = c("MRC","Year", "Original DB", "Processed-dates DB")) |> 
  kableExtra::kable_classic() |> 
  kableExtra::scroll_box(width="100%", height="200px") 

present_dates <- collisions_weather |>
  mutate(date = as.Date(stata_date)) |>
  filter(month(date) %in% 5:9) |>
  select(mrc, year, date)

weather_filled |>
  anti_join(present_dates, by = c("mrc", "year", "date")) |>
  arrange(mrc, year, date) |> 
  select(mrc, date) |> 
  knitr::kable(caption="Missing Dates in Collisions Database",
               col.names = c("MRC", "Dates")) |> 
  kableExtra::kable_classic() |> 
  kableExtra::scroll_box(width="100%", height="200px")

Duplicates?
# A tibble: 0 × 4
# ℹ 4 variables: mrc <dbl>, year <dbl>, date <date>, n <int>
How many rows the database should have approx?
[1] 17480
What we have 
[1] 17520
What we should have 
[1] 17594

Number of by MRC and year

MRC	Year	Original DB	Processed-dates DB
23	2020	152	153
43	2001	151	153
43	2005	152	153
43	2006	152	153
43	2010	151	153
43	2012	152	153
43	2013	151	153
43	2014	150	153
43	2015	151	153
43	2016	146	153
43	2017	146	153
43	2018	146	153
43	2019	143	152
43	2020	143	153
43	2021	144	153
43	2022	148	153
58	2000	152	153
58	2020	152	153
65	2000	152	153
65	2020	151	153

Missing Dates in Collisions Database

MRC	Dates
23	2020-05-10
43	2001-07-02
43	2001-09-19
43	2005-05-15
43	2006-09-17
43	2010-05-09
43	2010-09-06
43	2012-05-17
43	2013-08-03
43	2013-09-22
43	2014-07-20
43	2014-08-16
43	2014-09-07
43	2015-06-21
43	2015-08-09
43	2016-05-10
43	2016-05-25
43	2016-06-06
43	2016-06-29
43	2016-08-17
43	2016-08-18
43	2016-09-26
43	2017-05-30
43	2017-08-06
43	2017-08-08
43	2017-08-18
43	2017-08-24
43	2017-09-04
43	2017-09-24
43	2018-05-06
43	2018-07-18
43	2018-07-23
43	2018-08-02
43	2018-08-07
43	2018-08-29
43	2018-09-01
43	2019-05-03
43	2019-05-07
43	2019-05-11
43	2019-06-05
43	2019-07-22
43	2019-07-24
43	2019-08-14
43	2019-09-05
43	2019-09-22
43	2020-05-02
43	2020-05-03
43	2020-05-05
43	2020-05-06
43	2020-05-10
43	2020-06-01
43	2020-06-10
43	2020-07-22
43	2020-07-23
43	2020-09-25
43	2021-05-02
43	2021-05-17
43	2021-05-23
43	2021-06-23
43	2021-07-11
43	2021-07-18
43	2021-08-01
43	2021-08-16
43	2021-09-12
43	2022-05-09
43	2022-05-23
43	2022-07-11
43	2022-08-09
43	2022-09-05
58	2000-08-27
58	2020-05-23
65	2000-08-21
65	2020-05-04
65	2020-05-10

Given that there were some missing dates, we added the assuming cero counts in these days.

We also interpolated linearly missing values in weather variables.

collisions_weather_corr<- 
collisions |>
  mutate(rn= row_number())|> 
  #2025-06-13: added missing rows manually
  mutate(date = as.Date(yday(stata_date) - 1,
                        origin = paste0(year, "-01-01")))|>
      filter(month(date) %in% 5:9) |>
      group_by(mrc, year) |>
      complete(
          date = seq.Date(min(date), max(date), by = "day")
      )|>
      ungroup()|>
    mutate(
    across(
      c(nb_collisions, alcohol, velocidad,
        nb_mort_c, nb_blese_grave_c, nb_blese_leger_c,
        nb_vehicules),
      ~ if_else(is.na(stata_date), 0, .x)
    )
  )|> 
  dplyr::select(-month, -day_of_week, -stata_date)|> 
  mutate(date_num= unclass(date))|> 
  left_join(wx_summary_filt2002_22_all, by=c("date_num"="date_num", "mrc"="mrc"))|> 
  rename("date"="date.x")|> 
  group_by(mrc, date)|> 
  arrange(mrc, date)|>  
  ungroup()|> 
  mutate(tr_contr=ifelse(mrc %in% c(371, 23), "control", "treatment")) |>
  mutate(tr_contr_sens=ifelse(mrc %in% c(371, 23, 43), "control", "treatment"))|>  #adding Sherbrook
  left_join(races, by="year")|>
  #2025-06-25. missing weather variables for Longueuil
  #select(year, date, mrc, nearest, date_num)|> #2025-06-25= 2006 43 2006-09-24 to 2006-09-30; 2019 58 2019-05-01 to 2019-05-31
  mutate(date_num= unclass(date))|>
  left_join(offsets_db, by= c("nearest"="EQUALS", "year"="year"))|>  
  mutate(
    exposure      = coalesce(
      as.integer(dplyr::between(date, race_date - days(2),
                                 race_date + days(3))), 0L),
    exposure_alt7 = coalesce(
      as.integer(dplyr::between(date, race_date - days(2),
                                 race_date + days(7))), 0L),
    exposure_off  = coalesce(
      as.integer(dplyr::between(date, race_date,
                             race_date + days(3))), 0L),
    exposure_alt7_off = coalesce(
      as.integer(dplyr::between(date, race_date,
                             race_date + days(7))), 0L)
  )|>
  mutate(
    week_iso            = isoweek(date),
    year_f             = factor(year),
    mrc_f              = factor(mrc),
    exposure           = factor(exposure, levels = c(0, 1)),
    iso_yday           = yday(date),
    log_veh            = log(nb_vehicules + 1),  # adding 1 to avoid log(0)
    D                  = ifelse(tr_contr == "treatment" & exposure == 1, 1, 0),
    D_sens             = ifelse(tr_contr_sens == "treatment" & exposure == 1, 1, 0),
    D7                 = ifelse(tr_contr == "treatment" & exposure_alt7 == 1, 1, 0),
    D7_sens            = ifelse(tr_contr_sens == "treatment" & exposure_alt7 == 1, 1, 0),
    D_off              = ifelse(tr_contr == "treatment" & exposure_off == 1, 1, 0),
    D_sens_off         = ifelse(tr_contr_sens == "treatment" & exposure_off == 1, 1, 0),
    D7_off             = ifelse(tr_contr == "treatment" & exposure_alt7_off == 1, 1, 0),
    D7_sens_off        = ifelse(tr_contr_sens == "treatment" & exposure_alt7_off == 1, 1, 0)
  )|>
  group_by(mrc, year)|>
   arrange(date, .by_group = TRUE)|>
  mutate(across(
    c(min_temp_mean, min_temp_median, max_temp_mean, max_temp_median, 
total_precip_mean, total_precip_median),           # <- weather variables
    ~ zoo::na.approx(.x, x = date, na.rm = FALSE, rule = 2),
    .names = "{.col}_lin"                        # create *_lin instead overwriting
  ))|>
  ungroup()


if(filter(collisions_weather_corr, is.na(license_holders_sex_age)|is.na(vehicles_use_type)|is.na(vehicles_use_type_vehicle_age))|> nrow()){
  stop("Missing data in offsets")
  }


cat("Check the rows left after discarding missing observations in weather variables (not interpolated ones)\n")
collisions_weather_corr |>  filter(!is.na(min_temp_mean), !is.na(max_temp_mean), !is.na(total_precip_mean)) |> 
  nrow()

cat("Added the Lag precipitation mean (2 days) by MRC and year:\n")
collisions_weather_corr$lag_2_prec_median_lin <- 
collisions_weather_corr|> 
    group_by(mrc, year)|> 
    mutate(total_precip_median_lin_lag2 = dplyr::lag(total_precip_median_lin, 2, order_by = iso_yday))|> 
    ungroup()|>
    pull(total_precip_median_lin_lag2) 

if(
identical(collisions_weather_corr$total_precip_median_lin, collisions_weather_corr$lag_2_prec_median_lin)
){stop("The variable was not lagged")}

summary(collisions_weather_corr$lag_2_prec_median_lin)

cat("Lag precipitation mean (2 days) by MRC and year:\n")
table(is.na(collisions_weather_corr$lag_2_prec_median_lin))

cat("Lag precipitation mean, range of missing days:\n")
if(collisions_weather_corr|>  filter(is.na(lag_2_prec_median_lin)) |> nrow()>0){
collisions_weather_corr|>  filter(is.na(lag_2_prec_median_lin))|> dplyr::select(mrc, year, iso_yday)|> summarise(max=max(iso_yday), min= min(iso_yday))
}
cat("There ara no missing values\n")

cat("Added the cluster variable by MRC and year:\n")
collisions_weather_corr$cluster<- interaction(collisions_weather_corr$year, collisions_weather_corr$mrc)

cat("Add moving 3-day moving average\n")
Mean <- function(x) if (sum(!is.na(x)) >= 2) mean(x, na.rm = TRUE) else NA
#prior x elements (list(-seq(x)))
roll <- function(x)  zoo::rollapply(x, list(-seq(3)), Mean, fill = NA, partial = TRUE)
collisions_weather_corr <- 
transform(collisions_weather_corr, mov_ave_velocidad = ave(velocidad, cluster, FUN = roll))

cat("We generated the moving average of high-speed collisions, rounding to 0 decimals, and imputing the first two values (MRC-year) with the third value\n")
collisions_weather_corr<- 
collisions_weather_corr|> 
  dplyr::arrange(mrc, year, iso_yday)|> 
  #dplyr::select(mrc, year, cluster, iso_yday, velocidad, mov_ave_velocidad)|> 
  #filter(mrc!=23)|> 
    dplyr::group_by(cluster)|> 
    dplyr::mutate(
        third_value = nth(na.omit(mov_ave_velocidad), 1),
        mov_ave_velocidad = ifelse(is.na(mov_ave_velocidad), third_value, mov_ave_velocidad)
    )|>
    dplyr::select(-third_value)|> 
  dplyr::mutate(mov_ave_velocidad= round(mov_ave_velocidad, 0))|> 
  ungroup()


cat("Imputed with the value of the third period\n")
collisions_weather_corr$lag_2_prec_median_lin_imp <- 
collisions_weather_corr|>
    group_by(cluster)|>
    mutate(
        third_value = nth(na.omit(lag_2_prec_median_lin), 1),
        lag_2_prec_median_lin_imp = ifelse(is.na(lag_2_prec_median_lin), third_value, lag_2_prec_median_lin)
    )|>
    dplyr::select(-third_value)|>
    ungroup()|>
    pull(lag_2_prec_median_lin_imp)
if(
identical(collisions_weather_corr$lag_2_prec_median_lin, collisions_weather_corr$lag_2_prec_median_lin_imp)
){stop("The variable was not imputed")}
if(
identical(collisions_weather_corr$total_precip_median_lin, collisions_weather_corr$lag_2_prec_median_lin_imp)
){stop("The variable was not lagged")}


cat("Missing in imputed values of 2-day lagged precipitations\n")
collisions_weather_corr|> 
    filter(is.na(lag_2_prec_median_lin_imp))|> dplyr::select(mrc, year, iso_yday) |> nrow()

invisible("All are the first ranges")

cat("MRCs treated and controls and days of exposure:\n")
table(collisions_weather_corr$tr_contr, collisions_weather_corr$exposure)
cat("MRCs treated and controls and days of exposure(+ Sherbrooke as control):\n")
table(collisions_weather_corr$tr_contr_sens, collisions_weather_corr$exposure)

cat("Added the time variable: year and standardized day in the year\n")
collisions_weather_corr$time_id <- with(collisions_weather_corr, paste0(year_f, "_", iso_yday))


xtabs(~ year+mrc+tr_contr + exposure, data = collisions_weather_corr)|> 
  data.frame()|>
  filter(
    case_when(mrc%in% c(43, 58, 65, 66)& tr_contr=="control"& exposure==0~F,
    mrc%in% c(43, 58, 65, 66)& tr_contr=="control"& exposure==1~F,
    mrc== 23 & tr_contr=="treatment"& exposure==0~F,
    mrc== 23 & tr_contr=="treatment"& exposure==1~F,
                   T~T)) |> 
  arrange(tr_contr, mrc, year, exposure) |> 
  data.frame()|> 
  knitr::kable(caption="Number of observations by treatment status, MRC, year and exposure",
               col.names = c("Year", "MRC", "Control/Treated cities", "Days of exposure", "Observations"))|> 
  kableExtra::kable_classic() |> 
  kableExtra::scroll_box(width="100%", height="400px")

table(collisions_weather_corr$cluster, collisions_weather_corr$tr_contr) |> data.frame()|> 
  filter(Freq>0)|>
  knitr::kable(caption="Number of observations by MRC and year",
               col.names = c("MRC-Year", "Control/Treated cities", "Observations"))|> 
  kableExtra::kable_classic()|> 
  kableExtra::scroll_box(width="100%", height="400px")

Check the rows left after discarding missing observations in weather variables (not interpolated ones)
[1] 17489
Added the Lag precipitation mean (2 days) by MRC and year:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00    0.00    0.10    3.22    3.00  108.50     230 
Lag precipitation mean (2 days) by MRC and year:

FALSE  TRUE 
17364   230 
Lag precipitation mean, range of missing days:
# A tibble: 1 × 2
    max   min
  <dbl> <dbl>
1   123   121
There ara no missing values
Added the cluster variable by MRC and year:
Add moving 3-day moving average
We generated the moving average of high-speed collisions, rounding to 0 decimals, and imputing the first two values (MRC-year) with the third value
Imputed with the value of the third period
Missing in imputed values of 2-day lagged precipitations
[1] 0
MRCs treated and controls and days of exposure:
           
                0     1
  control    3399   120
  treatment 13595   480
MRCs treated and controls and days of exposure(+ Sherbrooke as control):
           
                0     1
  control    6797   240
  treatment 10197   360
Added the time variable: year and standardized day in the year

Number of observations by treatment status, MRC, year and exposure

Year	MRC	Control/Treated cities	Days of exposure	Observations
2000	23	control	0	147
2000	23	control	1	6
2001	23	control	0	147
2001	23	control	1	6
2002	23	control	0	147
2002	23	control	1	6
2003	23	control	0	147
2003	23	control	1	6
2004	23	control	0	147
2004	23	control	1	6
2005	23	control	0	147
2005	23	control	1	6
2006	23	control	0	147
2006	23	control	1	6
2007	23	control	0	147
2007	23	control	1	6
2008	23	control	0	147
2008	23	control	1	6
2009	23	control	0	153
2009	23	control	1	0
2010	23	control	0	147
2010	23	control	1	6
2011	23	control	0	147
2011	23	control	1	6
2012	23	control	0	147
2012	23	control	1	6
2013	23	control	0	147
2013	23	control	1	6
2014	23	control	0	147
2014	23	control	1	6
2015	23	control	0	147
2015	23	control	1	6
2016	23	control	0	147
2016	23	control	1	6
2017	23	control	0	147
2017	23	control	1	6
2018	23	control	0	147
2018	23	control	1	6
2019	23	control	0	147
2019	23	control	1	6
2020	23	control	0	153
2020	23	control	1	0
2021	23	control	0	153
2021	23	control	1	0
2022	23	control	0	147
2022	23	control	1	6
2000	43	treatment	0	147
2000	43	treatment	1	6
2001	43	treatment	0	147
2001	43	treatment	1	6
2002	43	treatment	0	147
2002	43	treatment	1	6
2003	43	treatment	0	147
2003	43	treatment	1	6
2004	43	treatment	0	147
2004	43	treatment	1	6
2005	43	treatment	0	147
2005	43	treatment	1	6
2006	43	treatment	0	147
2006	43	treatment	1	6
2007	43	treatment	0	147
2007	43	treatment	1	6
2008	43	treatment	0	147
2008	43	treatment	1	6
2009	43	treatment	0	153
2009	43	treatment	1	0
2010	43	treatment	0	147
2010	43	treatment	1	6
2011	43	treatment	0	147
2011	43	treatment	1	6
2012	43	treatment	0	147
2012	43	treatment	1	6
2013	43	treatment	0	147
2013	43	treatment	1	6
2014	43	treatment	0	147
2014	43	treatment	1	6
2015	43	treatment	0	147
2015	43	treatment	1	6
2016	43	treatment	0	147
2016	43	treatment	1	6
2017	43	treatment	0	147
2017	43	treatment	1	6
2018	43	treatment	0	147
2018	43	treatment	1	6
2019	43	treatment	0	146
2019	43	treatment	1	6
2020	43	treatment	0	153
2020	43	treatment	1	0
2021	43	treatment	0	153
2021	43	treatment	1	0
2022	43	treatment	0	147
2022	43	treatment	1	6
2000	58	treatment	0	147
2000	58	treatment	1	6
2001	58	treatment	0	147
2001	58	treatment	1	6
2002	58	treatment	0	147
2002	58	treatment	1	6
2003	58	treatment	0	147
2003	58	treatment	1	6
2004	58	treatment	0	147
2004	58	treatment	1	6
2005	58	treatment	0	147
2005	58	treatment	1	6
2006	58	treatment	0	147
2006	58	treatment	1	6
2007	58	treatment	0	147
2007	58	treatment	1	6
2008	58	treatment	0	147
2008	58	treatment	1	6
2009	58	treatment	0	153
2009	58	treatment	1	0
2010	58	treatment	0	147
2010	58	treatment	1	6
2011	58	treatment	0	147
2011	58	treatment	1	6
2012	58	treatment	0	147
2012	58	treatment	1	6
2013	58	treatment	0	147
2013	58	treatment	1	6
2014	58	treatment	0	147
2014	58	treatment	1	6
2015	58	treatment	0	147
2015	58	treatment	1	6
2016	58	treatment	0	147
2016	58	treatment	1	6
2017	58	treatment	0	147
2017	58	treatment	1	6
2018	58	treatment	0	147
2018	58	treatment	1	6
2019	58	treatment	0	147
2019	58	treatment	1	6
2020	58	treatment	0	153
2020	58	treatment	1	0
2021	58	treatment	0	153
2021	58	treatment	1	0
2022	58	treatment	0	147
2022	58	treatment	1	6
2000	65	treatment	0	147
2000	65	treatment	1	6
2001	65	treatment	0	147
2001	65	treatment	1	6
2002	65	treatment	0	147
2002	65	treatment	1	6
2003	65	treatment	0	147
2003	65	treatment	1	6
2004	65	treatment	0	147
2004	65	treatment	1	6
2005	65	treatment	0	147
2005	65	treatment	1	6
2006	65	treatment	0	147
2006	65	treatment	1	6
2007	65	treatment	0	147
2007	65	treatment	1	6
2008	65	treatment	0	147
2008	65	treatment	1	6
2009	65	treatment	0	153
2009	65	treatment	1	0
2010	65	treatment	0	147
2010	65	treatment	1	6
2011	65	treatment	0	147
2011	65	treatment	1	6
2012	65	treatment	0	147
2012	65	treatment	1	6
2013	65	treatment	0	147
2013	65	treatment	1	6
2014	65	treatment	0	147
2014	65	treatment	1	6
2015	65	treatment	0	147
2015	65	treatment	1	6
2016	65	treatment	0	147
2016	65	treatment	1	6
2017	65	treatment	0	147
2017	65	treatment	1	6
2018	65	treatment	0	147
2018	65	treatment	1	6
2019	65	treatment	0	147
2019	65	treatment	1	6
2020	65	treatment	0	153
2020	65	treatment	1	0
2021	65	treatment	0	153
2021	65	treatment	1	0
2022	65	treatment	0	147
2022	65	treatment	1	6
2000	66	treatment	0	147
2000	66	treatment	1	6
2001	66	treatment	0	147
2001	66	treatment	1	6
2002	66	treatment	0	147
2002	66	treatment	1	6
2003	66	treatment	0	147
2003	66	treatment	1	6
2004	66	treatment	0	147
2004	66	treatment	1	6
2005	66	treatment	0	147
2005	66	treatment	1	6
2006	66	treatment	0	147
2006	66	treatment	1	6
2007	66	treatment	0	147
2007	66	treatment	1	6
2008	66	treatment	0	147
2008	66	treatment	1	6
2009	66	treatment	0	153
2009	66	treatment	1	0
2010	66	treatment	0	147
2010	66	treatment	1	6
2011	66	treatment	0	147
2011	66	treatment	1	6
2012	66	treatment	0	147
2012	66	treatment	1	6
2013	66	treatment	0	147
2013	66	treatment	1	6
2014	66	treatment	0	147
2014	66	treatment	1	6
2015	66	treatment	0	147
2015	66	treatment	1	6
2016	66	treatment	0	147
2016	66	treatment	1	6
2017	66	treatment	0	147
2017	66	treatment	1	6
2018	66	treatment	0	147
2018	66	treatment	1	6
2019	66	treatment	0	147
2019	66	treatment	1	6
2020	66	treatment	0	153
2020	66	treatment	1	0
2021	66	treatment	0	153
2021	66	treatment	1	0
2022	66	treatment	0	147
2022	66	treatment	1	6

Number of observations by MRC and year

MRC-Year	Control/Treated cities	Observations
2000.23	control	153
2001.23	control	153
2002.23	control	153
2003.23	control	153
2004.23	control	153
2005.23	control	153
2006.23	control	153
2007.23	control	153
2008.23	control	153
2009.23	control	153
2010.23	control	153
2011.23	control	153
2012.23	control	153
2013.23	control	153
2014.23	control	153
2015.23	control	153
2016.23	control	153
2017.23	control	153
2018.23	control	153
2019.23	control	153
2020.23	control	153
2021.23	control	153
2022.23	control	153
2000.43	treatment	153
2001.43	treatment	153
2002.43	treatment	153
2003.43	treatment	153
2004.43	treatment	153
2005.43	treatment	153
2006.43	treatment	153
2007.43	treatment	153
2008.43	treatment	153
2009.43	treatment	153
2010.43	treatment	153
2011.43	treatment	153
2012.43	treatment	153
2013.43	treatment	153
2014.43	treatment	153
2015.43	treatment	153
2016.43	treatment	153
2017.43	treatment	153
2018.43	treatment	153
2019.43	treatment	152
2020.43	treatment	153
2021.43	treatment	153
2022.43	treatment	153
2000.58	treatment	153
2001.58	treatment	153
2002.58	treatment	153
2003.58	treatment	153
2004.58	treatment	153
2005.58	treatment	153
2006.58	treatment	153
2007.58	treatment	153
2008.58	treatment	153
2009.58	treatment	153
2010.58	treatment	153
2011.58	treatment	153
2012.58	treatment	153
2013.58	treatment	153
2014.58	treatment	153
2015.58	treatment	153
2016.58	treatment	153
2017.58	treatment	153
2018.58	treatment	153
2019.58	treatment	153
2020.58	treatment	153
2021.58	treatment	153
2022.58	treatment	153
2000.65	treatment	153
2001.65	treatment	153
2002.65	treatment	153
2003.65	treatment	153
2004.65	treatment	153
2005.65	treatment	153
2006.65	treatment	153
2007.65	treatment	153
2008.65	treatment	153
2009.65	treatment	153
2010.65	treatment	153
2011.65	treatment	153
2012.65	treatment	153
2013.65	treatment	153
2014.65	treatment	153
2015.65	treatment	153
2016.65	treatment	153
2017.65	treatment	153
2018.65	treatment	153
2019.65	treatment	153
2020.65	treatment	153
2021.65	treatment	153
2022.65	treatment	153
2000.66	treatment	153
2001.66	treatment	153
2002.66	treatment	153
2003.66	treatment	153
2004.66	treatment	153
2005.66	treatment	153
2006.66	treatment	153
2007.66	treatment	153
2008.66	treatment	153
2009.66	treatment	153
2010.66	treatment	153
2011.66	treatment	153
2012.66	treatment	153
2013.66	treatment	153
2014.66	treatment	153
2015.66	treatment	153
2016.66	treatment	153
2017.66	treatment	153
2018.66	treatment	153
2019.66	treatment	153
2020.66	treatment	153
2021.66	treatment	153
2022.66	treatment	153

• MRCs are the treated units

• MRCs 65, 66, and 58 are the treated units (Laval, Montreal, Longueuil)

• MRCs 23 and 43 are the control units (Quebec and Sherbrooke)

• 2009, 2020 and 2021 are years where races didnt occurr

We formatted the data recatangularily by subsetting 35 days before and 35 days after the race, for each combination of MRC and year.

collisions_weather_corr_rect <- 
  collisions_weather_corr|>
  group_by(cluster)|>
  mutate(day_diff = as.numeric(difftime(date, race_date, units = "days")))|> # Calculate difference in days
  mutate(race_window = dplyr::between(day_diff, -35, 35))|> # Check if difference is within -30 to 30 days
  ungroup()|>
  filter(race_window == TRUE)|>
  group_by(cluster)|>
  mutate(yday_corr = row_number())|>
  dplyr::select(-race_window, -day_diff)|> # Remove the temporary columns
  ungroup()  

cat(paste0("Number of rows: ", nrow(collisions_weather_corr_rect),"\n\n\n\n"))

Number of rows: 7100

Panel series

Collisions per MRC

df_collisions <- collisions_weather_corr|> 
  group_by(mrc_f, year, week_iso, exposure) %>%   # group by MRC, year and week
  summarise(
    nb_collisions = sum(nb_collisions),                         # count collisions
    .groups = "drop"
  )

#exposure band
exp_band <- df_collisions %>% 
    filter(exposure == 1) %>% 
    distinct(year, week_iso) %>%      # one row per (year, week) that has exposure
    mutate(
        xmin = week_iso - 0.5,            # band spans full week on x-axis
        xmax = week_iso + 0.5,
        ymin = -Inf,                      # full panel height
        ymax =  Inf
    )

ggplot(df_collisions, aes(x = week_iso, y = nb_collisions)) +
  geom_line(
    aes(group= mrc_f, colour = mrc_f), linetype = "solid"
  ) +
  geom_rect(
    data       = exp_band,
    aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
    inherit.aes = FALSE,
    fill   = "grey40",
    alpha  = 0.2
  ) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  scale_shape_manual(
    name   = "MRC",
    values = 0:(nlevels(df_collisions$mrc_f)-1)
  ) +
  facet_wrap(~ year, ncol = 6) +                 # <- one panel per year
  labs(
    x     = "ISO Week",
    y     = "Number of Collisions"
  ) +
  theme_minimal() +
  theme(
    panel.grid.minor = element_blank(),
    legend.position  = "right"
  )

Weekly Collisions per MRC (gray band where exposure = 1)

If they series look strange is because in the same exposure week might be a non-exposure count.

df_collisions_day <- collisions_weather_corr|> 
  group_by(mrc_f, year, iso_yday, exposure) %>%   # group by MRC, year and week
  summarise(
    nb_collisions = sum(nb_collisions),                         # count collisions
    .groups = "drop"
  )
exp_band_day <- df_collisions_day %>% 
    filter(exposure == 1) %>% 
    distinct(year, iso_yday) %>%      # one row per (year, week) that has exposure
    mutate(
        xmin = iso_yday - 0.5,            # band spans full week on x-axis
        xmax = iso_yday + 0.5,
        ymin = -Inf,                      # full panel height
        ymax =  Inf
    )

ggplot(df_collisions_day, aes(x = iso_yday, y = nb_collisions)) +
  geom_line(
    aes(group= mrc_f, colour = mrc_f), linetype = "solid"
  ) +
  geom_rect(
    data       = exp_band_day,
    aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
    inherit.aes = FALSE,
    fill   = "grey40",
    alpha  = 0.2
  ) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  scale_shape_manual(
    name   = "MRC",
    values = 0:(nlevels(df_collisions_day$mrc_f)-1)
  ) +
  facet_wrap(~ year, ncol = 6) +                 # <- one panel per year
  labs(
    x     = "ISO Day",
    y     = "Number of Collisions"
  ) +
  theme_minimal() +
  theme(
    panel.grid.minor = element_blank(),
    legend.position  = "right"
  )

Daily Collisions per MRC (gray band where exposure = 1)

df_collisions_day <- collisions_weather_corr|> 
  group_by(mrc_f, year, iso_yday, exposure) %>%   # group by MRC, year and week
  summarise(
    nb_collisions = sum(nb_collisions),                         # count collisions
    .groups = "drop"
  )
df_collisions_day$cluster<- interaction(df_collisions_day$mrc_f,df_collisions_day$year)
Mean <- function(x) if (sum(!is.na(x)) >= 2) mean(x, na.rm = TRUE) else NA
#prior x elements (list(-seq(x)))
roll <- function(x)  zoo::rollapply(x, list(-seq(3)), Mean, fill = NA, partial = TRUE)
df_collisions_day<- 
transform(df_collisions_day, roll = ave(nb_collisions, cluster, FUN = roll))

exp_band_day <- df_collisions_day %>% 
    filter(exposure == 1) %>% 
    distinct(year, iso_yday) %>%      # one row per (year, week) that has exposure
    mutate(
        xmin = iso_yday - 0.5,            # band spans full week on x-axis
        xmax = iso_yday + 0.5,
        ymin = -Inf,                      # full panel height
        ymax =  Inf
    )

ggplot(df_collisions_day, aes(x = iso_yday, y = roll)) +
  geom_line(
    aes(group= mrc_f, colour = mrc_f), linetype = "solid"
  ) +
  geom_rect(
    data       = exp_band_day,
    aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
    inherit.aes = FALSE,
    fill   = "grey40",
    alpha  = 0.2
  ) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  scale_shape_manual(
    name   = "MRC",
    values = 0:(nlevels(df_collisions_day$mrc_f)-1)
  ) +
  facet_wrap(~ year, ncol = 6) +                 # <- one panel per year
  labs(
    x     = "ISO Day",
    y     = "Number of Collisions"
  ) +
  theme_minimal() +
  theme(
    panel.grid.minor = element_blank(),
    legend.position  = "right"
  )

3-day moving average of Collisions per MRC (gray band where exposure = 1)

Alcohol-related collisions per MRC

df_alcohol <- collisions_weather_corr|> 
  group_by(mrc_f, year, week_iso, exposure) %>%   # group by MRC, year and week
  summarise(
    alcohol = sum(alcohol),                         # count collisions
    .groups = "drop"
  )

#exposure band
exp_band_oh <- df_alcohol %>% 
    filter(exposure == 1) %>% 
    distinct(year, week_iso) %>%      # one row per (year, week) that has exposure
    mutate(
        xmin = week_iso - 0.5,            # band spans full week on x-axis
        xmax = week_iso + 0.5,
        ymin = -Inf,                      # full panel height
        ymax =  Inf
    )

ggplot(df_alcohol, aes(x = week_iso, y = alcohol)) +
  geom_line(
    aes(group= mrc_f, colour = mrc_f), linetype = "solid"
  ) +
  geom_rect(
    data       = exp_band_oh,
    aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
    inherit.aes = FALSE,
    fill   = "grey40",
    alpha  = 0.2
  ) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  scale_shape_manual(
    name   = "MRC",
    values = 0:(nlevels(df_alcohol$mrc_f)-1)
  ) +
  facet_wrap(~ year, ncol = 6) +                 # <- one panel per year
  labs(
    x     = "ISO Week",
    y     = "Number of Collisions Related to Alcohol (log scaled)"
  ) +
  theme_minimal() +
  theme(
    panel.grid.minor = element_blank(),
    legend.position  = "right"
  )+
  scale_y_continuous(
    trans  = scales::pseudo_log_trans(base = 10),  # maneja n = 0
    breaks = c(0, 1, 2, 5, 10, 20, 50, 100),
    labels = scales::comma
  ) 

Weekly Alcohol-related collisions per MRC (gray band where exposure = 1)

df_alcohol_day <- collisions_weather_corr|> 
  group_by(mrc_f, year, iso_yday, exposure) %>%   # group by MRC, year and week
  summarise(
    alcohol = sum(alcohol),                         # count collisions
    .groups = "drop"
  )
exp_band_day_oh <- df_alcohol_day %>% 
    filter(exposure == 1) %>% 
    distinct(year, iso_yday) %>%      # one row per (year, week) that has exposure
    mutate(
        xmin = iso_yday - 0.5,            # band spans full week on x-axis
        xmax = iso_yday + 0.5,
        ymin = -Inf,                      # full panel height
        ymax =  Inf
    )

ggplot(df_alcohol_day, aes(x = iso_yday, y = alcohol)) +
  geom_line(
    aes(group= mrc_f, colour = mrc_f), linetype = "solid"
  ) +
  geom_rect(
    data       = exp_band_day,
    aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
    inherit.aes = FALSE,
    fill   = "grey40",
    alpha  = 0.2
  ) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  scale_shape_manual(
    name   = "MRC",
    values = 0:(nlevels(df_alcohol_day$mrc_f)-1)
  ) +
  facet_wrap(~ year, ncol = 6) +                 # <- one panel per year
  labs(
    x     = "ISO Day",
    y     = "Number of Collisions Related to Alcohol (log scaled)"
  ) +
  theme_minimal() +
  theme(
    panel.grid.minor = element_blank(),
    legend.position  = "right"
  )+
  scale_y_continuous(
    trans  = scales::pseudo_log_trans(base = 10),  # maneja n = 0
    breaks = c(0, 1, 2, 5, 10, 20, 50, 100),
    labels = scales::comma
  ) 

Daily Alcohol-related collisions per MRC (gray band where exposure = 1)

df_alcohol_day <- collisions_weather_corr|> 
  group_by(mrc_f, year, iso_yday, exposure) %>%   # group by MRC, year and week
  summarise(
    alcohol = sum(alcohol),                         # count collisions
    .groups = "drop"
  )

df_alcohol_day$cluster<- interaction(df_alcohol_day$mrc_f,df_alcohol_day$year)
Mean <- function(x) if (sum(!is.na(x)) >= 2) mean(x, na.rm = TRUE) else NA
#prior x elements (list(-seq(x)))
roll <- function(x)  zoo::rollapply(x, list(-seq(3)), Mean, fill = NA, partial = TRUE)
df_alcohol_day<- 
transform(df_alcohol_day, roll = ave(alcohol, cluster, FUN = roll))

exp_band_day_oh <- df_alcohol_day %>% 
    filter(exposure == 1) %>% 
    distinct(year, iso_yday) %>%      # one row per (year, week) that has exposure
    mutate(
        xmin = iso_yday - 0.5,            # band spans full week on x-axis
        xmax = iso_yday + 0.5,
        ymin = -Inf,                      # full panel height
        ymax =  Inf
    )

ggplot(df_alcohol_day, aes(x = iso_yday, y = roll)) +
  geom_line(
    aes(group= mrc_f, colour = mrc_f), linetype = "solid"
  ) +
  geom_rect(
    data       = exp_band_day,
    aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
    inherit.aes = FALSE,
    fill   = "grey40",
    alpha  = 0.2
  ) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  scale_shape_manual(
    name   = "MRC",
    values = 0:(nlevels(df_alcohol_day$mrc_f)-1)
  ) +
  facet_wrap(~ year, ncol = 6) +                 # <- one panel per year
  labs(
    x     = "ISO Day",
    y     = "Number of Collisions related to Alcohol (log scaled)"
  ) +
  theme_minimal() +
  theme(
    panel.grid.minor = element_blank(),
    legend.position  = "right"
  )+
  scale_y_continuous(
    trans  = scales::pseudo_log_trans(base = 10),  # maneja n = 0
    breaks = c(0, 1, 2, 5, 10, 20, 50, 100),
    labels = scales::comma
  ) 

3-day moving average of Alcohol-related collisions per MRC (gray band where exposure = 1)

High‑speed collisions per MRC

df_speed <- collisions_weather_corr |> 
  group_by(mrc_f, year, week_iso, exposure) %>%
  summarise(high_speed_tickets = sum(velocidad), .groups = "drop")

exp_band_speed <- df_speed %>%
  filter(exposure == 1) %>%
  distinct(year, week_iso) %>%
  mutate(xmin = week_iso - 0.5, xmax = week_iso + 0.5, ymin = -Inf, ymax = Inf)

ggplot(df_speed, aes(x = week_iso, y = high_speed_tickets)) +
  geom_line(aes(group = mrc_f, colour = mrc_f)) +
  geom_rect(data = exp_band_speed, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
            inherit.aes = FALSE, fill = "grey40", alpha = 0.2) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  facet_wrap(~ year, ncol = 6) +
  labs(x = "ISO Week", y = "Number of High‑speed Collisions (log scaled)") +
  theme_minimal() +
  theme(panel.grid.minor = element_blank(), legend.position = "right") +
  scale_y_continuous(trans = scales::pseudo_log_trans(base = 10),
                     breaks = c(0,1,2,5,10,20,50,100), labels = scales::comma)

Weekly High‑speed collisions per MRC (grey band where exposure = 1)

df_speed_day <- collisions_weather_corr |> 
  group_by(mrc_f, year, iso_yday, exposure) %>%
  summarise(high_speed_tickets = sum(velocidad), .groups = "drop")

exp_band_speed_day <- df_speed_day %>%
  filter(exposure == 1) %>%
  distinct(year, iso_yday) %>%
  mutate(xmin = iso_yday - 0.5, xmax = iso_yday + 0.5, ymin = -Inf, ymax = Inf)

ggplot(df_speed_day, aes(x = iso_yday, y = high_speed_tickets)) +
  geom_line(aes(group = mrc_f, colour = mrc_f)) +
  geom_rect(data = exp_band_speed_day, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
            inherit.aes = FALSE, fill = "grey40", alpha = 0.2) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  facet_wrap(~ year, ncol = 6) +
  labs(x = "ISO Day", y = "Number of High‑speed Collisions (log scaled)") +
  theme_minimal() +
  theme(panel.grid.minor = element_blank(), legend.position = "right") +
  scale_y_continuous(trans = scales::pseudo_log_trans(base = 10),
                     breaks = c(0,1,2,5,10,20,50,100), labels = scales::comma)

Daily High‑speed Collisions per MRC (grey band where exposure = 1)

High‑speed collisions per 100 vehicles by MRC

df_speed_per_vh <- collisions_weather_corr |> 
  group_by(mrc_f, year, week_iso, exposure) %>%
  reframe(high_speed_tickets_per_vh = (sum(velocidad, na.rm=T)/sum(vehicles_use_type, na.rm=T))*1e6, .groups = "drop")

exp_band_speed_per_vh <- df_speed_per_vh %>%
  filter(exposure == 1) %>%
  distinct(year, week_iso) %>%
  mutate(xmin = week_iso - 0.5, xmax = week_iso + 0.5, ymin = -Inf, ymax = Inf)

ggplot(df_speed_per_vh, aes(x = week_iso, y = high_speed_tickets_per_vh)) +
  geom_line(aes(group = mrc_f, colour = mrc_f)) +
  geom_rect(data = exp_band_speed_per_vh, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
            inherit.aes = FALSE, fill = "grey40", alpha = 0.2) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  facet_wrap(~ year, ncol = 6) +
  labs(x = "ISO Week", y = "Number of High‑speed Collisions (log scaled)") +
  theme_minimal() +
  theme(panel.grid.minor = element_blank(), legend.position = "right")  +
  scale_y_continuous(trans = scales::pseudo_log_trans(base = 10),
                     breaks = c(0,1,2,5,10,20,50,100), labels = scales::comma)

Weekly High‑speed collisions per 1 MM vehicles by MRC (grey band where exposure = 1)

df_speed_day_per_vh <- collisions_weather_corr |> 
  group_by(mrc_f, year, iso_yday, exposure) %>%
  reframe(high_speed_tickets_per_vh = (sum(velocidad, na.rm=T)/sum(vehicles_use_type, na.rm=T))*1e6, .groups = "drop")

exp_band_speed_day_per_vh <- df_speed_day_per_vh %>%
  filter(exposure == 1) %>%
  distinct(year, iso_yday) %>%
  mutate(xmin = iso_yday - 0.5, xmax = iso_yday + 0.5, ymin = -Inf, ymax = Inf)

ggplot(df_speed_day_per_vh, aes(x = iso_yday, y = high_speed_tickets_per_vh)) +
  geom_line(aes(group = mrc_f, colour = mrc_f)) +
  geom_rect(data = exp_band_speed_day_per_vh, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
            inherit.aes = FALSE, fill = "grey40", alpha = 0.2) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  facet_wrap(~ year, ncol = 6) +
  labs(x = "ISO Day", y = "Number of High‑speed Tickets per 100 vehicles (log scaled)") +
  theme_minimal() +
  theme(panel.grid.minor = element_blank(), legend.position = "right") +
  scale_y_continuous(trans = scales::pseudo_log_trans(base = 10),
                     breaks = c(0,1,2,5,10,20,50,100), labels = scales::comma)

Daily High‑speed collisions per 1MM vehicles by MRC (grey band where exposure = 1)

Fatalities per MRC

df_fatal <- collisions_weather_corr |> 
  group_by(mrc_f, year, week_iso, exposure) %>%
  summarise(fatalities = sum(nb_mort_c), .groups = "drop")

exp_band_fatal <- df_fatal %>%
  filter(exposure == 1) %>%
  distinct(year, week_iso) %>%
  mutate(xmin = week_iso - 0.5, xmax = week_iso + 0.5, ymin = -Inf, ymax = Inf)

ggplot(df_fatal, aes(x = week_iso, y = fatalities)) +
  geom_line(aes(group = mrc_f, colour = mrc_f)) +
  geom_rect(data = exp_band_fatal, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
            inherit.aes = FALSE, fill = "grey40", alpha = 0.2) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  facet_wrap(~ year, ncol = 6) +
  labs(x = "ISO Week", y = "Number of Fatalities (log scaled)") +
  theme_minimal() +
  theme(panel.grid.minor = element_blank(), legend.position = "right") +
  scale_y_continuous(trans = scales::pseudo_log_trans(base = 10),
                     breaks = c(0,1,2,5,10,20,50,100), labels = scales::comma)

Weekly Fatalities per MRC (grey band where exposure = 1)

df_fatal_day <- collisions_weather_corr |> 
  group_by(mrc_f, year, iso_yday, exposure) %>%
  summarise(fatalities = sum(nb_mort_c), .groups = "drop")

exp_band_fatal_day <- df_fatal_day %>%
  filter(exposure == 1) %>%
  distinct(year, iso_yday) %>%
  mutate(xmin = iso_yday - 0.5, xmax = iso_yday + 0.5, ymin = -Inf, ymax = Inf)

ggplot(df_fatal_day, aes(x = iso_yday, y = fatalities)) +
  geom_line(aes(group = mrc_f, colour = mrc_f)) +
  geom_rect(data = exp_band_fatal_day, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
            inherit.aes = FALSE, fill = "grey40", alpha = 0.2) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  facet_wrap(~ year, ncol = 6) +
  labs(x = "ISO Day", y = "Number of Fatalities (log scaled)") +
  theme_minimal() +
  theme(panel.grid.minor = element_blank(), legend.position = "right") +
  scale_y_continuous(trans = scales::pseudo_log_trans(base = 10),
                     breaks = c(0,1,2,5,10,20,50,100), labels = scales::comma)

Daily Fatalities per MRC (grey band where exposure = 1)

df_fatal_day <- collisions_weather_corr |> 
  group_by(mrc_f, year, iso_yday, exposure) %>%
  summarise(fatalities = sum(nb_mort_c), .groups = "drop")


df_fatal_day$cluster<- interaction(df_fatal_day$mrc_f,df_fatal_day$year)
Mean <- function(x) if (sum(!is.na(x)) >= 2) mean(x, na.rm = TRUE) else NA
#prior x elements (list(-seq(x)))
roll <- function(x)  zoo::rollapply(x, list(-seq(3)), Mean, fill = NA, partial = TRUE)
df_speed_day<- 
transform(df_fatal_day, roll = ave(fatalities, cluster, FUN = roll))

exp_band_fatal_day <- df_fatal_day %>%
  filter(exposure == 1) %>%
  distinct(year, iso_yday) %>%
  mutate(xmin = iso_yday - 0.5, xmax = iso_yday + 0.5, ymin = -Inf, ymax = Inf)

ggplot(df_fatal_day, aes(x = iso_yday, y = fatalities)) +
  geom_line(aes(group = mrc_f, colour = mrc_f)) +
  geom_rect(data = exp_band_fatal_day, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
            inherit.aes = FALSE, fill = "grey40", alpha = 0.2) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  facet_wrap(~ year, ncol = 6) +
  labs(x = "ISO Day", y = "Number of Fatalities (log scaled)") +
  theme_minimal() +
  theme(panel.grid.minor = element_blank(), legend.position = "right") +
  scale_y_continuous(trans = scales::pseudo_log_trans(base = 10),
                     breaks = c(0,1,2,5,10,20,50,100), labels = scales::comma)

3-day moving average Fatalities per MRC (grey band where exposure = 1)

Seriously injured per MRC

df_serious <- collisions_weather_corr |> 
  group_by(mrc_f, year, week_iso, exposure) %>%
  summarise(seriously_injured = sum(nb_blese_grave_c), .groups = "drop")

exp_band_serious <- df_serious %>%
  filter(exposure == 1) %>%
  distinct(year, week_iso) %>%
  mutate(xmin = week_iso - 0.5, xmax = week_iso + 0.5, ymin = -Inf, ymax = Inf)

ggplot(df_serious, aes(x = week_iso, y = seriously_injured)) +
  geom_line(aes(group = mrc_f, colour = mrc_f)) +
  geom_rect(data = exp_band_serious, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
            inherit.aes = FALSE, fill = "grey40", alpha = 0.2) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  facet_wrap(~ year, ncol = 6) +
  labs(x = "ISO Week", y = "Seriously Injured (log scale)") +
  theme_minimal() +
  theme(panel.grid.minor = element_blank(), legend.position = "right") +
  scale_y_continuous(trans = scales::pseudo_log_trans(base = 10),
                     breaks = c(0,1,2,5,10,20,50,100), labels = scales::comma)

Weekly Seriously Injured per MRC (grey band where exposure = 1)

df_serious_day <- collisions_weather_corr |> 
  group_by(mrc_f, year, iso_yday, exposure) %>%
  summarise(seriously_injured = sum(nb_blese_grave_c), .groups = "drop")

exp_band_serious_day <- df_serious_day %>%
  filter(exposure == 1) %>%
  distinct(year, iso_yday) %>%
  mutate(xmin = iso_yday - 0.5, xmax = iso_yday + 0.5, ymin = -Inf, ymax = Inf)

ggplot(df_serious_day, aes(x = iso_yday, y = seriously_injured)) +
  geom_line(aes(group = mrc_f, colour = mrc_f)) +
  geom_rect(data = exp_band_serious_day, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
            inherit.aes = FALSE, fill = "grey40", alpha = 0.2) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  facet_wrap(~ year, ncol = 6) +
  labs(x = "ISO Day", y = "Seriously Injured (log scale)") +
  theme_minimal() +
  theme(panel.grid.minor = element_blank(), legend.position = "right") +
  scale_y_continuous(trans = scales::pseudo_log_trans(base = 10),
                     breaks = c(0,1,2,5,10,20,50,100), labels = scales::comma)

Daily Seriously Injured per MRC (grey band where exposure = 1)

df_serious_day <- collisions_weather_corr |> 
  group_by(mrc_f, year, iso_yday, exposure) %>%
  summarise(seriously_injured = sum(nb_blese_grave_c), .groups = "drop")


df_serious_day$cluster<- interaction(df_serious_day$mrc_f,df_serious_day$year)
Mean <- function(x) if (sum(!is.na(x)) >= 2) mean(x, na.rm = TRUE) else NA
#prior x elements (list(-seq(x)))
roll <- function(x)  zoo::rollapply(x, list(-seq(3)), Mean, fill = NA, partial = TRUE)
df_serious_day<- 
transform(df_serious_day, roll = ave(seriously_injured, cluster, FUN = roll))

exp_band_serious_day <- df_serious_day %>%
  filter(exposure == 1) %>%
  distinct(year, iso_yday) %>%
  mutate(xmin = iso_yday - 0.5, xmax = iso_yday + 0.5, ymin = -Inf, ymax = Inf)

ggplot(df_serious_day, aes(x = iso_yday, y = seriously_injured)) +
  geom_line(aes(group = mrc_f, colour = mrc_f)) +
  geom_rect(data = exp_band_serious_day, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
            inherit.aes = FALSE, fill = "grey40", alpha = 0.2) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  facet_wrap(~ year, ncol = 6) +
  labs(x = "ISO Day", y = "Seriously Injured (log scale)") +
  theme_minimal() +
  theme(panel.grid.minor = element_blank(), legend.position = "right") +
  scale_y_continuous(trans = scales::pseudo_log_trans(base = 10),
                     breaks = c(0,1,2,5,10,20,50,100), labels = scales::comma)

3-day moving average Seriously Injured per MRC (grey band where exposure = 1)

Minor injuries per MRC

df_minor <- collisions_weather_corr |> 
  group_by(mrc_f, year, week_iso, exposure) %>%
  summarise(minor_injuries = sum(nb_blese_leger_c), .groups = "drop")

exp_band_minor <- df_minor %>%
  filter(exposure == 1) %>%
  distinct(year, week_iso) %>%
  mutate(xmin = week_iso - 0.5, xmax = week_iso + 0.5, ymin = -Inf, ymax = Inf)

ggplot(df_minor, aes(x = week_iso, y = minor_injuries)) +
  geom_line(aes(group = mrc_f, colour = mrc_f)) +
  geom_rect(data = exp_band_minor, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
            inherit.aes = FALSE, fill = "grey40", alpha = 0.2) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  facet_wrap(~ year, ncol = 6) +
  labs(x = "ISO Week", y = "Minor Injuries (log scale)") +
  theme_minimal() +
  theme(panel.grid.minor = element_blank(), legend.position = "right") +
  scale_y_continuous(trans = scales::pseudo_log_trans(base = 10),
                     breaks = c(0,1,2,5,10,20,50,100), labels = scales::comma)

Weekly Minor Injuries per MRC (grey band where exposure = 1)

df_minor_day <- collisions_weather_corr |> 
  group_by(mrc_f, year, iso_yday, exposure) %>%
  summarise(minor_injuries = sum(nb_blese_leger_c), .groups = "drop")

exp_band_minor_day <- df_minor_day %>%
  filter(exposure == 1) %>%
  distinct(year, iso_yday) %>%
  mutate(xmin = iso_yday - 0.5, xmax = iso_yday + 0.5, ymin = -Inf, ymax = Inf)

ggplot(df_minor_day, aes(x = iso_yday, y = minor_injuries)) +
  geom_line(aes(group = mrc_f, colour = mrc_f)) +
  geom_rect(data = exp_band_minor_day, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
            inherit.aes = FALSE, fill = "grey40", alpha = 0.2) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  facet_wrap(~ year, ncol = 6) +
  labs(x = "ISO Day", y = "Minor Injuries (log scale)") +
  theme_minimal() +
  theme(panel.grid.minor = element_blank(), legend.position = "right") +
  scale_y_continuous(trans = scales::pseudo_log_trans(base = 10),
                     breaks = c(0,1,2,5,10,20,50,100), labels = scales::comma)

Daily Minor Injuries per MRC (grey band where exposure = 1)

df_minor_day <- collisions_weather_corr |> 
  group_by(mrc_f, year, iso_yday, exposure) %>%
  summarise(minor_injuries = sum(nb_blese_leger_c), .groups = "drop")


df_minor_day$cluster<- interaction(df_minor_day$mrc_f,df_minor_day$year)
Mean <- function(x) if (sum(!is.na(x)) >= 2) mean(x, na.rm = TRUE) else NA
#prior x elements (list(-seq(x)))
roll <- function(x)  zoo::rollapply(x, list(-seq(3)), Mean, fill = NA, partial = TRUE)
df_speed_day<- 
transform(df_minor_day, roll = ave(minor_injuries, cluster, FUN = roll))

exp_band_minor_day <- df_minor_day %>%
  filter(exposure == 1) %>%
  distinct(year, iso_yday) %>%
  mutate(xmin = iso_yday - 0.5, xmax = iso_yday + 0.5, ymin = -Inf, ymax = Inf)

ggplot(df_minor_day, aes(x = iso_yday, y = minor_injuries)) +
  geom_line(aes(group = mrc_f, colour = mrc_f)) +
  geom_rect(data = exp_band_minor_day, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
            inherit.aes = FALSE, fill = "grey40", alpha = 0.2) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  facet_wrap(~ year, ncol = 6) +
  labs(x = "ISO Day", y = "Minor Injuries (log scale)") +
  theme_minimal() +
  theme(panel.grid.minor = element_blank(), legend.position = "right") +
  scale_y_continuous(trans = scales::pseudo_log_trans(base = 10),
                     breaks = c(0,1,2,5,10,20,50,100), labels = scales::comma)

3-day moving average Minor Injuries per MRC (grey band where exposure = 1)

Vehicle counts per MRC

df_vehicules <- collisions_weather_corr |> 
  group_by(mrc_f, year, week_iso, exposure) %>%
  summarise(nb_vehicules = sum(nb_vehicules), .groups = "drop")

exp_band_veh <- df_vehicules %>%
  filter(exposure == 1) %>%
  distinct(year, week_iso) %>%
  mutate(xmin = week_iso - 0.5, xmax = week_iso + 0.5, ymin = -Inf, ymax = Inf)

ggplot(df_vehicules, aes(x = week_iso, y = nb_vehicules)) +
  geom_line(aes(group = mrc_f, colour = mrc_f)) +
  geom_rect(data = exp_band_veh, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
            inherit.aes = FALSE, fill = "grey40", alpha = 0.2) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  facet_wrap(~ year, ncol = 6) +
  labs(x = "ISO Week", y = "Vehicle Counts") +
  theme_minimal() +
  theme(panel.grid.minor = element_blank(), legend.position = "right") 

Weekly Vehicle Counts per MRC (grey band where exposure = 1)

df_vehicules_day <- collisions_weather_corr |> 
  group_by(mrc_f, year, iso_yday, exposure) %>%
  summarise(nb_vehicules = sum(nb_vehicules), .groups = "drop")

exp_band_veh_day <- df_vehicules_day %>%
  filter(exposure == 1) %>%
  distinct(year, iso_yday) %>%
  mutate(xmin = iso_yday - 0.5, xmax = iso_yday + 0.5, ymin = -Inf, ymax = Inf)

ggplot(df_vehicules_day, aes(x = iso_yday, y = nb_vehicules)) +
  geom_line(aes(group = mrc_f, colour = mrc_f)) +
  geom_rect(data = exp_band_veh_day, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
            inherit.aes = FALSE, fill = "grey40", alpha = 0.2) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  facet_wrap(~ year, ncol = 6) +
  labs(x = "ISO Day", y = "Vehicle Counts") +
  theme_minimal() +
  theme(panel.grid.minor = element_blank(), legend.position = "right") 

Daily Vehicle Counts per MRC (grey band where exposure = 1)

df_vehicules_day <- collisions_weather_corr |> 
  group_by(mrc_f, year, iso_yday, exposure) %>%
  summarise(nb_vehicules = sum(nb_vehicules), .groups = "drop")


df_vehicules_day$cluster<- interaction(df_vehicules_day$mrc_f,df_vehicules_day$year)
Mean <- function(x) if (sum(!is.na(x)) >= 2) mean(x, na.rm = TRUE) else NA
#prior x elements (list(-seq(x)))
roll <- function(x)  zoo::rollapply(x, list(-seq(3)), Mean, fill = NA, partial = TRUE)
df_speed_day<- 
transform(df_vehicules_day, roll = ave(nb_vehicules, cluster, FUN = roll))

exp_band_veh_day <- df_vehicules_day %>%
  filter(exposure == 1) %>%
  distinct(year, iso_yday) %>%
  mutate(xmin = iso_yday - 0.5, xmax = iso_yday + 0.5, ymin = -Inf, ymax = Inf)

ggplot(df_vehicules_day, aes(x = iso_yday, y = nb_vehicules)) +
  geom_line(aes(group = mrc_f, colour = mrc_f)) +
  geom_rect(data = exp_band_veh_day, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
            inherit.aes = FALSE, fill = "grey40", alpha = 0.2) +
  scale_colour_brewer(palette = "Dark2", name = "MRC") +
  facet_wrap(~ year, ncol = 6) +
  labs(x = "ISO Day", y = "Vehicle Counts") +
  theme_minimal() +
  theme(panel.grid.minor = element_blank(), legend.position = "right") 

3-day moving average Vehicle Counts per MRC (grey band where exposure = 1)

Test panel structure

#https://cran.r-project.org/web/packages/plm/vignettes/A_plmPackage.html

E <- plm::pdata.frame(collisions_weather_corr_rect|>
                        mutate(sum_veh=vehicles_use_type, Y= (velocidad/vehicles_use_type)*1e6), 
                      index=c("cluster","yday_corr"), drop.index=TRUE, row.names=TRUE)

cat("Fixed effects (two–way: individual + time)\n")
fixef_model <- plm(Y ~ D+ min_temp_mean_lin+ max_temp_mean_lin+ total_precip_median_lin+ lag_2_prec_median_lin_imp,#+ offset(vehicles_use_type), #not needed
           data = E, model = "within", index = "townid")
summary(fixef_model)

cat("Robust coefficients\n")
lmtest::coeftest(fixef_model,
         vcov = function(x) vcovSCC(x, type = "HC1", maxlag = 2))

cat("Random effects (two–way)\n")
re_tw <- plm(
  Y ~ D + min_temp_mean_lin + max_temp_mean_lin +
        total_precip_median_lin + lag_2_prec_median_lin_imp,#+ offset(vehicles_use_type), #not needed
  data   = E,
  model  = "random",
  effect = "twoways"
)
summary(re_tw)

 phtest(fixef_model, re_tw) 
#   Hausman Test
# 
# data:  Y ~ D + min_temp_mean_lin + max_temp_mean_lin + total_precip_median_lin +  ...
# chisq = 22.392, df = 5, p-value = 0.000441
# alternative hypothesis: one model is inconsistent

cat("Wooldridge test (first-order, idiosyncratic)\n")
pbgtest(fixef_model)

# Breusch-Godfrey/Wooldridge test for serial correlation in panel models
# 
# data:  Y ~ D + min_temp_mean_lin + max_temp_mean_lin + total_precip_median_lin +  ...
# chisq = 202.71, df = 71, p-value = 1.321e-14
# alternative hypothesis: serial correlation in idiosyncratic errors

cat("Breusch–Godfrey (panel)    \n")
pwartest(fixef_model)
#   Wooldridge's test for serial correlation in FE panels
# 
# data:  fixef_model
# F = 0.3438, df1 = 1, df2 = 6998, p-value = 0.5577
# alternative hypothesis: serial correlation

cat("BP / White (panel) \n")
lmtest::bptest(fixef_model)
#   studentized Breusch-Pagan test
# 
# data:  fixef_model
# BP = 27.318, df = 5, p-value = 0.00004946

plmtest(fixef_model, type = "bp")
#   Lagrange Multiplier Test - (Breusch-Pagan)
# 
# data:  Y ~ D + min_temp_mean_lin + max_temp_mean_lin + total_precip_median_lin +  ...
# chisq = 8552.8, df = 1, p-value < 2.2e-16
# alternative hypothesis: significant effects

cat("cross-section dependence\n")
pcdtest(fixef_model, test = "cd") 
#   Pesaran CD test for cross-sectional dependence in panels
# 
# data:  Y ~ D + min_temp_mean_lin + max_temp_mean_lin + total_precip_median_lin +     lag_2_prec_median_lin_imp
#z = 3.2052, p-value = 0.00135
# alternative hypothesis: cross-sectional dependence

Fixed effects (two–way: individual + time)
Oneway (individual) effect Within Model

Call:
plm(formula = Y ~ D + min_temp_mean_lin + max_temp_mean_lin + 
    total_precip_median_lin + lag_2_prec_median_lin_imp, data = E, 
    model = "within", index = "townid")

Balanced Panel: n = 100, T = 71, N = 7100

Residuals:
    Min.  1st Qu.   Median  3rd Qu.     Max. 
-3.94882 -0.94737 -0.38854  0.67581 17.96750 

Coefficients:
                             Estimate  Std. Error t-value    Pr(>|t|)    
D                          0.26205923  0.08514964  3.0776    0.002095 ** 
min_temp_mean_lin          0.00731450  0.00668995  1.0934    0.274275    
max_temp_mean_lin          0.01188647  0.00599374  1.9831    0.047390 *  
total_precip_median_lin    0.01634397  0.00340616  4.7984 0.000001633 ***
lag_2_prec_median_lin_imp -0.00021014  0.00323096 -0.0650    0.948145    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Total Sum of Squares:    22380
Residual Sum of Squares: 22210
R-Squared:      0.0075944
Adj. R-Squared: -0.0071605
F-statistic: 10.7058 on 5 and 6995 DF, p-value: 2.8684e-10
Robust coefficients

t test of coefficients:

                             Estimate  Std. Error t value  Pr(>|t|)    
D                          0.26205923  0.07455394  3.5150 0.0004425 ***
min_temp_mean_lin          0.00731450  0.00703739  1.0394 0.2986653    
max_temp_mean_lin          0.01188647  0.00553930  2.1458 0.0319198 *  
total_precip_median_lin    0.01634397  0.00403703  4.0485 0.0000521 ***
lag_2_prec_median_lin_imp -0.00021014  0.00283921 -0.0740 0.9410026    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Random effects (two–way)
Twoways effects Random Effect Model 
   (Swamy-Arora's transformation)

Call:
plm(formula = Y ~ D + min_temp_mean_lin + max_temp_mean_lin + 
    total_precip_median_lin + lag_2_prec_median_lin_imp, data = E, 
    effect = "twoways", model = "random")

Balanced Panel: n = 100, T = 71, N = 7100

Effects:
                  var std.dev share
idiosyncratic 3.15832 1.77717 0.841
individual    0.57982 0.76146 0.154
time          0.01663 0.12894 0.004
theta: 0.7331 (id) 0.1906 (time) 0.1857 (total)

Residuals:
    Min.  1st Qu.   Median  3rd Qu.     Max. 
-3.29104 -1.02471 -0.56762  0.69640 18.26990 

Coefficients:
                             Estimate  Std. Error z-value  Pr(>|z|)    
(Intercept)                0.90530936  0.13365594  6.7734 1.258e-11 ***
D                          0.25917852  0.09986462  2.5953  0.009451 ** 
min_temp_mean_lin          0.00834633  0.00690027  1.2096  0.226445    
max_temp_mean_lin          0.00931032  0.00603389  1.5430  0.122829    
total_precip_median_lin    0.01573930  0.00342091  4.6009 4.206e-06 ***
lag_2_prec_median_lin_imp -0.00066677  0.00325165 -0.2051  0.837528    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Total Sum of Squares:    22640
Residual Sum of Squares: 22501
R-Squared:      0.0061403
Adj. R-Squared: 0.0054398
Chisq: 43.8282 on 5 DF, p-value: 2.5099e-08

    Hausman Test

data:  Y ~ D + min_temp_mean_lin + max_temp_mean_lin + total_precip_median_lin +  ...
chisq = 22.392, df = 5, p-value = 0.000441
alternative hypothesis: one model is inconsistent

Wooldridge test (first-order, idiosyncratic)

    Breusch-Godfrey/Wooldridge test for serial correlation in panel models

data:  Y ~ D + min_temp_mean_lin + max_temp_mean_lin + total_precip_median_lin +  ...
chisq = 202.71, df = 71, p-value = 1.321e-14
alternative hypothesis: serial correlation in idiosyncratic errors

Breusch–Godfrey (panel) 

    Wooldridge's test for serial correlation in FE panels

data:  fixef_model
F = 0.3438, df1 = 1, df2 = 6998, p-value = 0.5577
alternative hypothesis: serial correlation

BP / White (panel)  

    studentized Breusch-Pagan test

data:  fixef_model
BP = 27.318, df = 5, p-value = 0.00004946


    Lagrange Multiplier Test - (Breusch-Pagan)

data:  Y ~ D + min_temp_mean_lin + max_temp_mean_lin + total_precip_median_lin +  ...
chisq = 8552.8, df = 1, p-value < 2.2e-16
alternative hypothesis: significant effects

cross-section dependence

    Pesaran CD test for cross-sectional dependence in panels

data:  Y ~ D + min_temp_mean_lin + max_temp_mean_lin + total_precip_median_lin +     lag_2_prec_median_lin_imp
z = 3.2052, p-value = 0.00135
alternative hypothesis: cross-sectional dependence

• Hausman test indicates that Fixed effects is consistent but FE not.

• Breusch-Pagan indicates that panel structure is needed, given unobserved heterogeneity, FE shoul be used

• Studentized Breusch-Pagan test indicates that corss-secitonal heteroskedasticity is present

• Cross section dependence also noted, so there may be correlated units.

• There is a clear first-order autorregressive structure (Wooldridge’s test), and a more general serial correlation (Breusch-Godfrey/Wooldridge test).

acf(residuals(fixef_model), lag.max=50)
pacf(residuals(fixef_model), lag.max = 50)

Box.test(residuals(fixef_model), lag = 10, type = "Ljung-Box")
Box.test(residuals(fixef_model), lag = 20, type = "Ljung-Box")
Box.test(residuals(fixef_model), lag = 30, type = "Ljung-Box")

    Box-Ljung test

data:  residuals(fixef_model)
X-squared = 29.327, df = 10, p-value = 0.001103


    Box-Ljung test

data:  residuals(fixef_model)
X-squared = 87.783, df = 20, p-value = 1.805e-10


    Box-Ljung test

data:  residuals(fixef_model)
X-squared = 101.36, df = 30, p-value = 1.131e-09

ACF plots

ACF plots

Several Box–Ljung tests up to lag 10, 20 and 30 rejected white-noise residuals (p < 0.001), indicating serial correlation beyond autorregressive first-order structure. Partial autocorrelation plots show spikes at 3, 4, 10, 11, 14, 18, 19, 20, 25, 38, and 43, this is why there is a direct autocorrelation, despite there is no single lagged structure that dominates. It is recommended that we adjust on the lagged outcome at these spikes.

Session info

cat(paste0("R library: ", Sys.getenv("R_LIBS_USER")))
cat(paste0("Date: ",withr::with_locale(new = c('LC_TIME' = 'C'), code =Sys.time())))
cat(paste0("Editor context: ", getwd()))
cat("quarto version: "); system("quarto --version") 

quarto::quarto_version()

save.image("_data/step1.RData")
saveRDS(collisions_weather_corr, file = "_data/collisions_weather.rds", ascii = FALSE, version = NULL, compress = TRUE, refhook = NULL)

R library: H:/Mi unidad/PERSONAL ANDRES/UCH_salud_publica/pasantia/f1/f1/renv/library/windows/R-4.4/x86_64-w64-mingw32Date: 2025-07-07 17:15:40.278964Editor context: H:/Mi unidad/PERSONAL ANDRES/UCH_salud_publica/pasantia/f1/f1quarto version: [1] 0
[1] '1.6.39'

sesion_info <- devtools::session_info()

Warning in system2("quarto", "-V", stdout = TRUE, env = paste0("TMPDIR=", : el
comando ejecutado '"quarto"
TMPDIR=C:/Users/andre/AppData/Local/Temp/Rtmp0ydj1m/file40584ccb7d06 -V' tiene
el estatus 1

dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) |> 
 knitr::kable(caption = "R packages", format = "html",
      col.names = c("Row number", "Package", "Version"),
    row.names = FALSE,
      align = c("c", "l", "r")) |> 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 12)|> 
  kableExtra::scroll_box(width = "100%", height = "375px")  

R packages

Row number	Package	Version
abind	1.4-8	RSPM
assertthat	0.2.1	RSPM
backports	1.5.0	RSPM
bayesplot	1.12.0	RSPM
bdsmatrix	1.3-7	RSPM
bit	4.6.0	CRAN (R 4.4.3)
bit64	4.6.0-1	CRAN (R 4.4.3)
Boom	0.9.15	RSPM
BoomSpikeSlab	1.2.6	RSPM
boot	1.3-30	CRAN (R 4.4.1)
bpmn	0.1.0	Github (bergant/bpmn@628d3efaa27544c221b2fd7f1895301a63b70c49)
bridgesampling	1.1-2	RSPM
brms	2.22.0	RSPM
Brobdingnag	1.2-9	RSPM
broom	1.0.8	RSPM
bsts	0.9.10	RSPM
cachem	1.1.0	CRAN (R 4.4.3)
car	3.1-3	RSPM
carData	3.0-5	RSPM
CausalImpact	1.3.0	RSPM
CBPS	0.23	RSPM
checkmate	2.3.2	RSPM
cli	3.6.5	RSPM
coda	0.19-4.1	RSPM
codetools	0.2-20	CRAN (R 4.4.1)
collapse	2.1.2	RSPM
colorspace	2.1-1	RSPM
conquer	1.3.3	RSPM
crayon	1.5.3	CRAN (R 4.4.3)
cubature	2.1.4	RSPM
curl	6.2.3	CRAN (R 4.4.1)
CVXR	1.0-15	RSPM
dagitty	0.3-4	RSPM
data.table	1.17.4	RSPM
devtools	2.4.5	RSPM
DHARMa	0.4.7	RSPM
digest	0.6.37	RSPM
distributional	0.5.0	RSPM
doParallel	1.0.17	RSPM
doRNG	1.8.6.2	RSPM
doSNOW	1.0.20	RSPM
dplyr	1.1.4	RSPM
dreamerr	1.5.0	RSPM
ECOSolveR	0.5.5	RSPM
ellipsis	0.3.2	RSPM
emmeans	1.11.1	RSPM
estimability	1.5.1	RSPM
evaluate	1.0.3	RSPM
farver	2.1.2	RSPM
fastDummies	1.7.5	RSPM
fastmap	1.2.0	CRAN (R 4.4.3)
fect	1.0.0	RSPM
fixest	0.12.1	RSPM
forcats	1.0.0	RSPM
foreach	1.5.2	RSPM
forecast	8.24.0	RSPM
Formula	1.2-5	RSPM
fracdiff	1.5-3	RSPM
fs	1.6.6	RSPM
future	1.49.0	RSPM
future.apply	1.11.3	RSPM
geeM	0.10.1	RSPM
geepack	1.3.12	RSPM
generics	0.1.4	RSPM
geosphere	1.5-20	RSPM
GGally	2.2.1	RSPM
ggdag	0.2.13	RSPM
ggplot2	3.5.2	RSPM
ggstats	0.9.0	RSPM
glmmTMB	1.1.11	RSPM
glmnet	4.1-9	RSPM
glmx	0.2-1	RSPM
globals	0.18.0	RSPM
glue	1.8.0	RSPM
gmp	0.7-5	RSPM
gnm	1.1-5	RSPM
gridExtra	2.3	RSPM
gtable	0.3.6	RSPM
gtools	3.9.5	RSPM
haven	2.5.4	RSPM
hms	1.1.3	CRAN (R 4.4.3)
htmltools	0.5.8.1	RSPM
htmlwidgets	1.6.4	RSPM
httpuv	1.6.16	RSPM
httr	1.4.7	CRAN (R 4.4.3)
httr2	1.1.2	RSPM
igraph	2.1.4	RSPM
iterators	1.0.14	RSPM
jsonlite	2.0.0	CRAN (R 4.4.3)
kableExtra	1.4.0	RSPM
kernlab	0.9-33	RSPM
knitr	1.50	RSPM
labeling	0.4.3	RSPM
later	1.4.2	RSPM
lattice	0.22-6	CRAN (R 4.4.1)
lfe	3.1.1	RSPM
lifecycle	1.0.4	RSPM
listenv	0.9.1	RSPM
lme4	1.1-37	RSPM
lmtest	0.9-40	RSPM
loo	2.8.0	RSPM
lubridate	1.9.4	RSPM
magrittr	2.0.3	RSPM
MASS	7.3-60.2	CRAN (R 4.4.1)
MatchIt	4.7.2	RSPM
Matrix	1.7-0	CRAN (R 4.4.1)
MatrixModels	0.5-4	RSPM
matrixStats	1.5.0	RSPM
maxLik	1.5-2.1	RSPM
memoise	2.0.1	CRAN (R 4.4.3)
mgcv	1.9-1	CRAN (R 4.4.1)
mime	0.13	CRAN (R 4.4.3)
miniUI	0.1.2	RSPM
minqa	1.2.8	RSPM
miscTools	0.6-28	RSPM
mvtnorm	1.3-3	RSPM
nixtlar	0.6.2	RSPM
nlme	3.1-164	CRAN (R 4.4.1)
nloptr	2.2.1	RSPM
nnet	7.3-19	CRAN (R 4.4.1)
np	0.60-18	RSPM
numDeriv	2016.8-1.1	RSPM
openxlsx	4.2.8	RSPM
optimx	2025-4.9	RSPM
PanelMatch	3.1.1	RSPM
parallelly	1.44.0	RSPM
pillar	1.10.2	RSPM
pkgbuild	1.4.8	RSPM
pkgconfig	2.0.3	RSPM
pkgload	1.4.0	RSPM
plm	2.6-6	RSPM
plyr	1.8.9	RSPM
posterior	1.6.1	RSPM
pracma	2.4.4	CRAN (R 4.4.1)
processx	3.8.6	RSPM
profvis	0.4.0	RSPM
promises	1.3.2	RSPM
ps	1.9.1	RSPM
purrr	1.0.4	RSPM
Qtools	1.5.9	RSPM
quadprog	1.5-8	RSPM
quantdr	1.2.2	RSPM
quantmod	0.4.28	RSPM
quantreg	6.1	RSPM
quarto	1.4.4	RSPM
qvcalc	1.0.4	RSPM
R.methodsS3	1.8.2	RSPM
R.oo	1.27.1	RSPM
R.utils	2.13.0	RSPM
R6	2.6.1	RSPM
rappdirs	0.3.3	CRAN (R 4.4.3)
rbibutils	2.3	RSPM
RColorBrewer	1.1-3	RSPM
Rcpp	1.0.14	RSPM
RcppParallel	5.1.10	RSPM
Rdpack	2.6.4	RSPM
readr	2.1.5	CRAN (R 4.4.3)
reformulas	0.4.1	RSPM
relimp	1.0-5	RSPM
remotes	2.5.0	RSPM
renv	1.1.2	CRAN (R 4.4.1)
reshape2	1.4.4	RSPM
rgenoud	5.9-0.11	RSPM
rio	1.2.3	RSPM
rlang	1.1.6	RSPM
rmarkdown	2.29	RSPM
Rmpfr	1.1-0	RSPM
rngtools	1.5.2	RSPM
rstantools	2.4.0	RSPM
rstudioapi	0.17.1	RSPM
sandwich	3.1-1	RSPM
scales	1.4.0	RSPM
scpi	3.0.0	RSPM
sessioninfo	1.2.3	RSPM
shape	1.4.6.1	RSPM
shiny	1.10.0	RSPM
snow	0.4-4	RSPM
sp	2.2-0	RSPM
SparseM	1.84-2	RSPM
stringi	1.8.7	RSPM
stringmagic	1.2.0	RSPM
stringr	1.5.1	RSPM
survival	3.6-4	CRAN (R 4.4.1)
svglite	2.2.1	RSPM
Synth	1.1-8	RSPM
systemfonts	1.2.3	RSPM
tensorA	0.36.2.1	RSPM
textshaping	1.0.1	RSPM
tibble	3.2.1	RSPM
tidygraph	1.3.1	RSPM
tidyr	1.3.1	RSPM
tidyselect	1.2.1	RSPM
tidyverse	2.0.0	RSPM
timechange	0.3.0	RSPM
timeDate	4041.110	RSPM
TMB	1.9.17	RSPM
tseries	0.10-58	RSPM
TTR	0.24.4	RSPM
tzdb	0.5.0	CRAN (R 4.4.3)
urca	1.3-4	RSPM
urlchecker	1.0.1	RSPM
usethis	3.1.0	RSPM
utf8	1.2.5	RSPM
V8	6.0.3	RSPM
vctrs	0.6.5	RSPM
viridisLite	0.4.2	RSPM
vroom	1.6.5	CRAN (R 4.4.3)
weathercan	0.7.3.9000	https://ropensci.r-universe.dev (R 4.4.3)
webshot	0.5.5	RSPM
withr	3.0.2	RSPM
xfun	0.52	RSPM
xml2	1.3.8	CRAN (R 4.4.3)
xtable	1.8-4	RSPM
xts	0.14.1	RSPM
yaml	2.3.10	RSPM
zip	2.3.3	RSPM
zoo	1.8-14	RSPM

reticulate::py_list_packages()%>% 
 knitr::kable(caption = "Python packages", format = "html",
      col.names = c("Package", "Version", "Requirement"),
    row.names = FALSE,
      align = c("c", "l", "r", "r"))%>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 12)|>
  kableExtra::scroll_box(width = "100%", height = "375px")  

Python packages

Package	Version	Requirement
absl-py	2.1.0	absl-py==2.1.0
asttokens	2.4.1	asttokens==2.4.1
astunparse	1.6.3	astunparse==1.6.3
audioconverter	2.0.3	audioconverter==2.0.3
autograd	1.6.2	autograd==1.6.2
autograd-gamma	0.5.0	autograd-gamma==0.5.0
beautifulsoup4	4.12.3	beautifulsoup4==4.12.3
Brotli	1.1.0	Brotli==1.1.0
certifi	2023.11.17	certifi==2023.11.17
cffi	1.16.0	cffi==1.16.0
charset-normalizer	3.3.2	charset-normalizer==3.3.2
clarabel	0.9.0	clarabel==0.9.0
click	8.1.7	click==8.1.7
cloudpickle	3.0.0	cloudpickle==3.0.0
colorama	0.4.6	colorama==0.4.6
comm	0.2.1	comm==0.2.1
contourpy	1.2.0	contourpy==1.2.0
cvxopt	1.3.2	cvxopt==1.3.2
cvxpy	1.5.2	cvxpy==1.5.2
cycler	0.12.1	cycler==0.12.1
debugpy	1.8.0	debugpy==1.8.0
decorator	4.4.2	decorator==4.4.2
delete-chrome-history-py	0.1.8	delete-chrome-history-py==0.1.8
easyocr	1.7.1	easyocr==1.7.1
ecos	2.0.13	ecos==2.0.13
editdistance	0.8.1	editdistance==0.8.1
efficientnet	1.0.0	efficientnet==1.0.0
essential-generators	1.0	essential-generators==1.0
et-xmlfile	1.1.0	et-xmlfile==1.1.0
executing	2.0.1	executing==2.0.1
fancyimpute	0.7.0	fancyimpute==0.7.0
ffmpeg	1.4	ffmpeg==1.4
ffmpeg-python	0.2.0	ffmpeg-python==0.2.0
filedir	0.0.3	filedir==0.0.3
filelock	3.13.1	filelock==3.13.1
flatbuffers	24.3.25	flatbuffers==24.3.25
fonttools	4.47.2	fonttools==4.47.2
formulaic	1.0.1	formulaic==1.0.1
fsspec	2023.12.2	fsspec==2023.12.2
future	0.18.3	future==0.18.3
gast	0.6.0	gast==0.6.0
git-filter-repo	2.45.0	git-filter-repo==2.45.0
google-pasta	0.2.0	google-pasta==0.2.0
graphviz	0.20.3	graphviz==0.20.3
grpcio	1.65.4	grpcio==1.65.4
gTTS	2.5.1	gTTS==2.5.1
h5py	3.11.0	h5py==3.11.0
idna	3.6	idna==3.6
imageio	2.34.2	imageio==2.34.2
imageio-ffmpeg	0.5.1	imageio-ffmpeg==0.5.1
imgaug	0.4.0	imgaug==0.4.0
iniconfig	2.0.0	iniconfig==2.0.0
interface-meta	1.3.0	interface-meta==1.3.0
ipykernel	6.29.5	ipykernel==6.29.5
ipython	8.20.0	ipython==8.20.0
jedi	0.19.1	jedi==0.19.1
Jinja2	3.1.3	Jinja2==3.1.3
joblib	1.4.0	joblib==1.4.0
jupyter_client	8.6.0	jupyter_client==8.6.0
jupyter_core	5.7.1	jupyter_core==5.7.1
keras	3.4.1	keras==3.4.1
Keras-Applications	1.0.8	Keras-Applications==1.0.8
keras-ocr	0.9.3	keras-ocr==0.9.3
kiwisolver	1.4.5	kiwisolver==1.4.5
knnimpute	0.1.0	knnimpute==0.1.0
lazy_loader	0.4	lazy_loader==0.4
libclang	18.1.1	libclang==18.1.1
lifelines	0.28.0	lifelines==0.28.0
llvmlite	0.41.1	llvmlite==0.41.1
Markdown	3.6	Markdown==3.6
markdown-it-py	3.0.0	markdown-it-py==3.0.0
MarkupSafe	2.1.4	MarkupSafe==2.1.4
matplotlib	3.8.2	matplotlib==3.8.2
matplotlib-inline	0.1.6	matplotlib-inline==0.1.6
mdurl	0.1.2	mdurl==0.1.2
mido	1.3.3	mido==1.3.3
ml-dtypes	0.4.0	ml-dtypes==0.4.0
more-itertools	10.2.0	more-itertools==10.2.0
moviepy	1.0.3	moviepy==1.0.3
mpmath	1.3.0	mpmath==1.3.0
multipledispatch	1.0.0	multipledispatch==1.0.0
mutagen	1.47.0	mutagen==1.47.0
namex	0.0.8	namex==0.0.8
natsort	8.4.0	natsort==8.4.0
nest-asyncio	1.5.9	nest-asyncio==1.5.9
networkx	3.2.1	networkx==3.2.1
ninja	1.11.1.1	ninja==1.11.1.1
nose	1.3.7	nose==1.3.7
numba	0.58.1	numba==0.58.1
numexpr	2.10.0	numexpr==2.10.0
numpy	1.26.3	numpy==1.26.3
openai-whisper	20231117	openai-whisper==20231117
opencv-python	4.10.0.84	opencv-python==4.10.0.84
opencv-python-headless	4.10.0.84	opencv-python-headless==4.10.0.84
openpyxl	3.1.4	openpyxl==3.1.4
opt-einsum	3.3.0	opt-einsum==3.3.0
optree	0.12.1	optree==0.12.1
osqp	0.6.5	osqp==0.6.5
packaging	23.2	packaging==23.2
pandas	2.2.0	pandas==2.2.0
pandas-flavor	0.6.0	pandas-flavor==0.6.0
parso	0.8.3	parso==0.8.3
patsy	0.5.6	patsy==0.5.6
pillow	10.2.0	pillow==10.2.0
platformdirs	4.1.0	platformdirs==4.1.0
pluggy	1.5.0	pluggy==1.5.0
polars	1.9.0	polars==1.9.0
proglog	0.1.10	proglog==0.1.10
prompt-toolkit	3.0.43	prompt-toolkit==3.0.43
protobuf	4.25.4	protobuf==4.25.4
psutil	5.9.8	psutil==5.9.8
pure-eval	0.2.2	pure-eval==0.2.2
pyarrow	15.0.0	pyarrow==15.0.0
pyclipper	1.3.0.post5	pyclipper==1.3.0.post5
pycparser	2.22	pycparser==2.22
pycryptodomex	3.20.0	pycryptodomex==3.20.0
pydotplus	2.0.2	pydotplus==2.0.2
pydub	0.24.1	pydub==0.24.1
Pygments	2.17.2	Pygments==2.17.2
pyjanitor	0.26.0	pyjanitor==0.26.0
PyMuPDF	1.24.9	PyMuPDF==1.24.9
PyMuPDFb	1.24.9	PyMuPDFb==1.24.9
pyparsing	3.1.1	pyparsing==3.1.1
PyPDF2	3.0.1	PyPDF2==3.0.1
pyreadr	0.5.0	pyreadr==0.5.0
pytesseract	0.3.10	pytesseract==0.3.10
pytest	8.3.1	pytest==8.3.1
python-bidi	0.6.0	python-bidi==0.6.0
python-dateutil	2.8.2	python-dateutil==2.8.2
pytube	15.0.0	pytube==15.0.0
pytube3	9.6.4	pytube3==9.6.4
pytz	2023.3.post1	pytz==2023.3.post1
pywin32	306	pywin32==306
PyYAML	6.0.1	PyYAML==6.0.1
pyzmq	25.1.2	pyzmq==25.1.2
qdldl	0.1.7.post1	qdldl==0.1.7.post1
regex	2023.12.25	regex==2023.12.25
requests	2.32.3	requests==2.32.3
rich	13.7.1	rich==13.7.1
rpy2	3.5.16	rpy2==3.5.16
scikit-image	0.24.0	scikit-image==0.24.0
scikit-learn	1.3.2	scikit-learn==1.3.2
scikit-survival	0.22.2	scikit-survival==0.22.2
scipy	1.11.4	scipy==1.11.4
scs	3.2.6	scs==3.2.6
seaborn	0.13.2	seaborn==0.13.2
semantic-version	2.10.0	semantic-version==2.10.0
setuptools-rust	1.8.1	setuptools-rust==1.8.1
shapely	2.0.5	shapely==2.0.5
six	1.16.0	six==1.16.0
soupsieve	2.5	soupsieve==2.5
SpeechRecognition	3.10.1	SpeechRecognition==3.10.1
spyder-kernels	2.5.2	spyder-kernels==2.5.2
stack-data	0.6.3	stack-data==0.6.3
statsmodels	0.14.1	statsmodels==0.14.1
sympy	1.12	sympy==1.12
target	0.0.11	target==0.0.11
tensorboard	2.17.0	tensorboard==2.17.0
tensorboard-data-server	0.7.2	tensorboard-data-server==0.7.2
tensorflow	2.17.0	tensorflow==2.17.0
tensorflow-intel	2.17.0	tensorflow-intel==2.17.0
tensorflow-io-gcs-filesystem	0.31.0	tensorflow-io-gcs-filesystem==0.31.0
termcolor	2.4.0	termcolor==2.4.0
threadpoolctl	3.4.0	threadpoolctl==3.4.0
tifffile	2024.7.24	tifffile==2024.7.24
tiktoken	0.5.2	tiktoken==0.5.2
torch	2.4.0	torch==2.4.0
torchaudio	2.4.0	torchaudio==2.4.0
torchvision	0.19.0	torchvision==0.19.0
tornado	6.4	tornado==6.4
tqdm	4.66.1	tqdm==4.66.1
traitlets	5.14.1	traitlets==5.14.1
translator	0.0.9	translator==0.0.9
typing_extensions	4.9.0	typing_extensions==4.9.0
tzdata	2023.4	tzdata==2023.4
tzlocal	5.2	tzlocal==5.2
urllib3	2.1.0	urllib3==2.1.0
validators	0.33.0	validators==0.33.0
watchdog	3.0.0	watchdog==3.0.0
wcwidth	0.2.13	wcwidth==0.2.13
websockets	12.0	websockets==12.0
Werkzeug	3.0.3	Werkzeug==3.0.3
whisper	1.1.10	whisper==1.1.10
wrapt	1.16.0	wrapt==1.16.0
xarray	2024.1.1	xarray==2024.1.1
youtube-dl	2021.12.17	youtube-dl==2021.12.17
yt-dlp	2024.7.9	yt-dlp==2024.7.9

References

1.

LaZerte SE, Albers S. weathercan: Download and format weather data from environment and climate change canada. The Journal of Open Source Software. 2018;3(22):571. https://joss.theoj.org/papers/10.21105/joss.00571.

2.

Canadian Racer. Cancelled grands prix of canada – 1975 and 1987. https://www.canadianracer.com/cdn-gp-cancel.asp. Published 2003.

3.

Environment and Climate Change Canada. Glossary - Climate - Environment and Climate Change Canada. March 2025.